{"title": "A Semantic-Oriented Federated Learning for Hybrid Ground–Aqua Computing Systems", "authors": ["Benedetta Picano", "Romano Fantacci"], "year": 2023, "publication_date": "2023-10-19", "doi": "10.1109/jiot.2023.3325289", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "6", "pages": "10095-10103", "abstract": "Nowadays, an ambitious target of the next generation networks is to develop intelligent overarching space-air-ground-aqua computing systems, in order to provide a smart ecosystem able to efficiently operate computation in heterogeneous domains. In particular, in such a context, the underwater environment requires a special attention, since it is recognized as the most challenging domain, due to channel impairments and adverse propagation conditions. This paper proposes a self-intelligent system able to efficiently perform underwater environment monitoring or underwater survey of critical infrastructure, by resorting to the use of the semantic communication paradigm to lower the impairments due to the underwater channel propagation conditions. In particular, in our case, images sent by underwater devices are collected by shore small base stations (SSBSs) to form their training dataset to take part in a federated learning process with a ground base station. In particular, the paper considers a semantic communication scheme based on a deep-convolution neural networks encoder-decoder architecture for an efficient exploitation of the data transmission from underwater devices to the linked SSBSs. Performance analysis is provided to show the better behavior of the proposed system in comparison with the conventional alternative that does not involve the use of the semantic communications approach. Finally, a specific performance evaluation analysis is devoted to the investigation of the convergence behavior of the proposed federated learning procedure in reference to the cross ground-aqua system considered in order to highlight its advantages with respect to a classical implementation.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3325289", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4387789776", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10288204.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10288204.pdf", "record_type": "article", "screening_status": "core", "screening_priority": "medium", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2023_Picano_A_Semantic_Oriented_Federated_Learning_for_Hybrid_GroundAqua_Computing_Systems.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 630982, "sha256": "1233a1eeecee33f588c33ba99508b3602512a360f2ca4d7bd92d295ef0d7cf27", "download_source": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10288204.pdf", "pdf_pages": 9, "pdf_title_token_match": 1.0}
{"title": "Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission", "authors": ["Bingyan Xie", "Yongpeng Wu", "Yuxuan Shi", "Derrick Wing Kwan Ng", "Wenjun Zhang"], "year": 2023, "publication_date": "2023-08-14", "doi": "10.1109/jiot.2023.3304650", "venue": "IEEE Internet of Things Journal", "volume": "10", "issue": "24", "pages": "22555-22568", "abstract": "Multinode communication, which refers to the interaction among multiple devices, has attracted lots of attention in many Internet of Things (IoT) scenarios. However, its huge amounts of data flows and inflexibility for task extension have triggered the urgent requirement of communication-efficient distributed data transmission frameworks. In this article, inspired by the great superiorities on bandwidth reduction and task adaptation of semantic communications, we propose a federated learning (FL)-based semantic communication (FLSC) framework for multitask distributed image transmission with IoT devices. FL enables the design of independent semantic communication link of each user while further improves the semantic extraction and task performance through global aggregation. Each link in FLSC is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator for coarse-to-fine semantic extraction and meaning translation according to specific tasks. In order to extend the FLSC into more realistic conditions, we design a channel state information-based multiple-input–multiple-output transmission module to combat channel fading and noise. Simulation results show that the coarse semantic information can deal with a range of image-level tasks. Moreover, especially in low signal-to-noise ratio (SNR) and channel bandwidth ratio regimes, FLSC evidently outperforms the traditional scheme, e.g., about 10 peak SNR gain in the 3-dB channel condition.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3304650", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4385819970", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2023_Xie_Communication_Efficient_Framework_for_Distributed_Image_Semantic_Wireless_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1272649, "sha256": "0faed88f060064a2d87b00a5fd5fe370cee05caf14c0aa262a401110f00dd2e4", "download_source": "https://arxiv.org/pdf/2308.03713v2", "arxiv_id": "2308.03713", "pdf_pages": 14, "pdf_title_token_match": 0.875}
{"title": "Cooperative Resource Management in Quantum Key Distribution (QKD) Networks for Semantic Communication", "authors": ["Rakpong Kaewpuang", "Minrui Xu", "Wei Yang Bryan Lim", "Dusit Tao Niyato", "Han Yu", "Jiawen Kang", "Xuemin Shen"], "year": 2023, "publication_date": "2023-08-03", "doi": "10.1109/jiot.2023.3301033", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "3", "pages": "4454-4469", "abstract": "The increasing focus on privacy and security in 6G networks, which are intelligence-native, necessitates the use of quantum key distribution-secured semantic information communication (QKD-SIC) to protect confidential data. In QKD-SIC systems, edge devices connected via quantum channels can efficiently encrypt semantic information from the semantic source, and securely transmit the encrypted semantic information to the semantic destination. In this article, we consider an efficient resource (i.e., quantum key distribution (QKD) and KM wavelengths) sharing problem to support QKD-SIC systems under the uncertainty of semantic information generated by edge devices. In such a system, QKD service providers offer QKD services with different subscription options to the edge devices. The QKD services are envisioned to follow cloud computing that has the subscription in the reservation and on-demand options, i.e., for long and short (immediate) terms, respectively. As such, to reduce the cost for the edge device users, we propose a QKD resource management framework for the edge devices communicating semantic information. The framework is based on a two-stage stochastic optimization model to achieve optimal QKD deployment. Moreover, to reduce the deployment cost of QKD service providers, QKD resources in the proposed framework can be utilized based on efficient QKD-SIC resource management, including semantic information transmission among edge devices, secret-key provisioning, and cooperation formation among QKD service providers. In detail, the formulated two-stage stochastic optimization model can achieve the optimal QKD-SIC resource deployment while meeting the secret-key requirements for semantic information transmission of edge devices. Moreover, to share the cost of the QKD resource pool among cooperative QKD service providers forming a coalition in a fair and interpretable manner, the proposed framework leverages the concept of Shapley value from cooperative game theory as a solution. Experimental results demonstrate that the proposed framework can reduce the deployment cost by about 40% compared with existing noncooperative baselines.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3301033", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4385521771", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2023_Kaewpuang_Cooperative_Resource_Management_in_Quantum_Key_Distribution_QKD_Networks_for_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1200867, "sha256": "92857f6046a048aff029cd64416f3e417545c85e139d1098667f861e4fe84e15", "download_source": "https://arxiv.org/pdf/2209.11957v1", "arxiv_id": "2209.11957", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "High-Resolution Data Acquisition and Joint Source-Channel Coding in Underwater IoT", "authors": ["Vidyasagar Sadhu", "Zhile Li", "Zhuoran Qi", "Dario Pompili"], "year": 2023, "publication_date": "2023-01-25", "doi": "10.1109/jiot.2023.3239580", "venue": "IEEE Internet of Things Journal", "volume": "10", "issue": "16", "pages": "14003-14013", "abstract": "Reliable and persistent water monitoring is a challenging problem in smart underwater Internet of Things (UW IoT) due to its harsh, unexplored, and unpredictable nature. Given the need for high-resolution spatio–temporal sensing in such environments, traditional digital sensors are not suitable due to their high-cost, high-power consumption, and nonbiodegradable nature. Further, reliable and low-latency communication techniques that avoid data packet retransmissions, if the feedback is available, are crucial for reconstructing the phenomenon being monitored in a timely manner at the fusion center, such as a drone. To address the above challenges, we propose a novel architecture consisting of a substrate of densely deployed underwater all-analog biodegradable sensors that enable persistent sensing and continually transmitting data to the surface digital buoys. The analog nodes are designed to be energy efficient by implementing analog joint source-channel coding (JSCC), a low-complexity compression-communication technique, using biodegradable field effect transistors (FETs). We, then, propose a correlation-aware hybrid automatic repeat request (HARQ) technique to transmit data from the surface buoys to the fusion center. Such HARQ technique leverages JSCC and the redundancy in the buoy data (arising from the correlation of the phenomenon at the analog nodes) to avoid retransmissions, thus, saving energy and time.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3239580", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "deep joint source-channel coding", "joint source-channel coding", "semantic-aware channel coding"], "openalex_id": "https://openalex.org/W4317987713", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Joint Source-Channel Coding", "joint source-channel coding", "JSCC"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2023_Sadhu_High_Resolution_Data_Acquisition_and_Joint_Source_Channel_Coding_in_Underwater_IoT.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3854042, "sha256": "f7ff4aab36cfc51cc6950ea1db8193329a415279149011233e4f663cfbe716c9", "ieee_document_id": "10025809", "ieee_document_url": "https://ieeexplore.ieee.org/document/10025809", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10025809", "pdf_pages": 11, "pdf_title_token_match": 1.0}
{"title": "Joint Task and Data-Oriented Semantic Communications: A Deep Separate Source-Channel Coding Scheme", "authors": ["Jianhao Huang", "Dongxu Li", "Chuan Zhen Huang", "Xiaoqi Qin", "Wei Zhang"], "year": 2023, "publication_date": "2023-07-07", "doi": "10.1109/jiot.2023.3293154", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "2", "pages": "2255-2272", "abstract": "Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and semantic tasks, joint data compression and semantic analysis has become a pivotal issue in semantic communications. This article proposes a deep separate source-channel coding (DSSCC) framework for the joint task and data-oriented semantic communications (JTD-SCs) and utilizes the variational autoencoder approach to solve the rate-distortion problem with semantic distortion. First, by analyzing the Bayesian model of the DSSCC framework, we derive a novel rate-distortion optimization problem via the Bayesian inference approach for general data distributions and semantic tasks. Next, for a typical application of joint image transmission and classification, we combine the variational autoencoder approach with a forward adaption scheme to effectively extract image features and adaptively learn the density information of the obtained features. Finally, an iterative training algorithm is proposed to tackle the overfitting issue of deep learning models. Simulation results reveal that the proposed scheme achieves better coding gain as well as data recovery and classification performance in most scenarios, compared to the classical compression schemes and the emerging deep joint source-channel schemes.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3293154", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "task-oriented communication", "goal-oriented communication", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4383503581", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["deep joint source-channel", "semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2023_Huang_Joint_Task_and_Data_Oriented_Semantic_Communications_A_Deep_Separate_Source_Channel_Coding_Scheme.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2973261, "sha256": "b9497ae0b396db183a7f043f499f5769831b135b5b0f9019ee316c8b319ca0e2", "download_source": "https://arxiv.org/pdf/2302.13580v2", "arxiv_id": "2302.13580", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "Lattice-Based Analog Mappings for Low-Latency Wireless Sensor Networks", "authors": ["Pedro Suárez-Casal", "Óscar Fresnedo", "Darian Pérez-Adán", "Luis Castedo"], "year": 2023, "publication_date": "2023-05-04", "doi": "10.1109/jiot.2023.3273194", "venue": "IEEE Internet of Things Journal", "volume": "10", "issue": "19", "pages": "17137-17154", "abstract": "We consider the transmission of spatially correlated analog information in a wireless sensor network (WSN) through fading single-input and multiple-output (SIMO) multiple access channels (MACs) with low-latency requirements. A lattice-based analog joint source-channel coding (JSCC) approach is considered where vectors of consecutive source symbols are encoded at each sensor using an$n$-dimensional lattice and then transmitted to a multiantenna central node. We derive a minimum mean square error (MMSE) decoder that accounts for both the multidimensional structure of the encoding lattices and the spatial correlation. In addition, a sphere decoder is considered to simplify the required searches over the multidimensional lattices. Different lattice-based mappings are approached and the impact of their size and density on performance and latency is analyzed. Results show that, while meeting low-latency constraints, lattice-based analog JSCC provides performance gains and higher reliability with respect to the state-of-the-art JSCC schemes.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3273194", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4372037262", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2401.16952", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["joint source-channel coding", "JSCC"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2023_Suarez_Casal_Lattice_Based_Analog_Mappings_for_Low_Latency_Wireless_Sensor_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1077523, "sha256": "69a2bee6aa4447bd7c9603a80b942346d3c231ab433b55fba5eaf7eb14e3e65a", "download_source": "https://arxiv.org/pdf/2401.16952", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "Online Resource Allocation for Semantic-Aware Edge Computing Systems", "authors": ["Yihan Cang", "Ming Chen", "Zhaohui Yang", "Yuntao Hu", "Yinlu Wang", "Chongwen Huang", "Zhaoyang Zhang"], "year": 2023, "publication_date": "2023-10-17", "doi": "10.1109/jiot.2023.3325320", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "17", "pages": "28094-28110", "abstract": "Mobile edge computing (MEC) in the next generation networks will provide computation services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. However, the performance of MEC cannot be guaranteed, when large size local tasks are uploaded to the server simultaneously causing network congestion. As a new paradigm that focuses on transmitting the meaning of messages, semantic communications reveals the significant potential to reduce the network traffic. In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying long-term delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3325320", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4387717464", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2023_Cang_Online_Resource_Allocation_for_Semantic_Aware_Edge_Computing_Systems.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5271434, "sha256": "0aa4070f09ca45842bb45d30d8b4c2aa72142dd71985cfa7db43107941dfcc61", "download_source": "https://arxiv.org/pdf/2309.13917v1", "arxiv_id": "2309.13917", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "Semantic and Logical Communication-Control Codesign for Correlated Dynamical Systems", "authors": ["Abanoub M. Girgis", "Hyowoon Seo", "Jihong Park", "Mehdi Bennis"], "year": 2023, "publication_date": "2023-11-28", "doi": "10.1109/jiot.2023.3337109", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "7", "pages": "12631-12648", "abstract": "In this study, we delve into the intricacies of semantic communication-control codesign (CoCoCo) for wireless mixed logical dynamical (MLD) systems operating under signal temporal logic (STL) specifications. Our novel contribution, the MLD-Koopman autoencoder (AE), emerges as a method to linearize the progression of system states within a feature space. This linearization effectively mitigates the communication and computation costs associated with MLD system control. To surmount the challenges posed by multiple correlated MLD systems that possess distinct logical control rules while sharing baseline dynamics, we present the compositional logical dynamical (CLD)-Koopman AE as a remedy to the scalability limitations of the MLD-Koopman AE. This innovative approach incorporates two pivotal models—the dynamics semantic Koopman (DSK) model, capturing semantic correlations among MLD systems, and the logical semantic Koopman (LSK) model, encoding logical control rules. These models portray the linear evolution of baseline dynamics and control rules within a feature space, facilitating predictions of future states for multiple MLD systems with constrained communication. Validation comes from simulations on large-scale inverted cart-pole systems, demonstrating the prowess of the CLD-Koopman AE in achieving an average state prediction performance 82.77% higher than other predictive benchmarks, particularly evident at a signal-to-noise ratio (SNR) of 10 dB.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3337109", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4389076520", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://figshare.com/articles/journal_contribution/Semantic_and_Logical_Communication-Control_Co-Design_for_Correlated_Dynamical_Systems/24939234", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2023_Girgis_Semantic_and_Logical_Communication_Control_Codesign_for_Correlated_Dynamical_Systems.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2171247, "sha256": "83e553b8971d6117748fc8c024eadbf8918e2d458a14c8d503dd461d88704833", "ieee_document_id": "10330567", "ieee_document_url": "https://ieeexplore.ieee.org/document/10330567", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10330567", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "YOLO-Based Semantic Communication With Generative AI-Aided Resource Allocation for Digital Twins Construction", "authors": ["Baoxia Du", "Hongyang Du", "Haifeng Liu", "Dusit Tao Niyato", "Xin Long Peng", "Jun Yu", "Mingyang Qi", "You Xi Tang"], "year": 2023, "publication_date": "2023-09-20", "doi": "10.1109/jiot.2023.3317629", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "5", "pages": "7664-7678", "abstract": "Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this article, we propose a semantic communication framework based on you only look once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the acrlong AI-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new efficient layer aggregation network-horNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services.", "publisher_url": "https://doi.org/10.1109/jiot.2023.3317629", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication"], "openalex_id": "https://openalex.org/W4386883058", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2023_Du_YOLO_Based_Semantic_Communication_With_Generative_AI_Aided_Resource_Allocation_for_Digital_Twins_Construction.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 19129223, "sha256": "d4dc4aba04ba923afe1c14aa48a4697b237b74a2a2bf0f320821697cfd3a08c7", "download_source": "https://arxiv.org/pdf/2306.14138v1", "arxiv_id": "2306.14138", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "A Joint Communication and Computation Design for Distributed RIS-Assisted Probabilistic Semantic Communication in IIoT", "authors": ["Zhouxiang Zhao", "Zhaohui Yang", "Chongwen Huang", "Li Wei", "Qianqian Yang", "Caijun Zhong", "Wei Xu", "Zhaoyang Zhang"], "year": 2024, "publication_date": "2024-06-05", "doi": "10.1109/jiot.2024.3409271", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "16", "pages": "26568-26579", "abstract": "The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3409271", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication"], "openalex_id": "https://openalex.org/W4399376062", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2404.19750", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "semantic-aware transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2024_Zhao_A_Joint_Communication_and_Computation_Design_for_Distributed_RIS_Assisted_Probabilistic_Semantic_Communication_i.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 840182, "sha256": "9087193b7ca432e1a9a83e8f8c46fa8edd609f8e07cf76832c682a951c70170e", "download_source": "https://arxiv.org/pdf/2404.19750", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "A Perceptually Motivated Approach for Low-Complexity Speech Semantic Communication", "authors": ["Xiaojiao Chen", "Jing Wang", "Liang Xu", "Jingxuan Huang", "Zesong Fei"], "year": 2024, "publication_date": "2024-03-25", "doi": "10.1109/jiot.2024.3378779", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "12", "pages": "22054-22065", "abstract": "Deep learning-based semantic communication is an emerging communication method that achieves cooperative transmission between source and channel. The primary objectives of semantic communication are to enhance the efficiency of information transmission and ensure the accurate restoration of semantic content. Recent studies have shown that semantic communication performs well in enhancing transmission rates, especially in low signal-to-noise ratio environments. However, existing speech semantic communication methods neglect to account for speech perception at the receiver and the complexity of the method, which limits the practical implementation of semantic communication methods. In this paper, we propose a perceptually-motivated, low-complexity speech semantic communication method. Specifically, we employ an end-to-end communication approach to transmit the source speech and obtain the reconstructed speech at the receiver. To ensure the accurate extraction of semantic information, we present a low-complexity fully convolutional semantic encoder, which increases the accuracy of semantic information extraction and improves transmission efficiency. Considering the sensitivity of human perception, a multi-resolution joint loss function has been implemented to enhance the model’s performance and guarantee that the reconstructed speech aligns with the human ear’s auditory perception. Experimental results show that the proposed method performs better on objective and subjective metrics than existing speech transmission methods. Compared with existing neural semantic transmission methods, we improve the transmission efficiency, and the number of symbols needed for transmission is decreased by 60% without compromising the quality of speech. Furthermore, the proposed semantic communication method has a lower complexity and consumes less time to transmit.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3378779", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4393140342", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Chen_A_Perceptually_Motivated_Approach_for_Low_Complexity_Speech_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 7177543, "sha256": "c356a005f6752f1262bf61b7ec94eb94aa454ebdb5dc28e7b03e8b078f5eb537", "ieee_document_id": "10477917", "ieee_document_url": "https://ieeexplore.ieee.org/document/10477917", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10477917", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "A Semantic Communication Approach for Multiscene Target Detection in Intelligent Vehicle Networks", "authors": ["Zhiping Wan", "Shaojiang Liu", "Zhiming Xu", "Weichuan Ni", "Shitong Ye", "Feng Wang"], "year": 2024, "publication_date": "2024-03-19", "doi": "10.1109/jiot.2024.3378003", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "22", "pages": "35877-35890", "abstract": "As intelligent vehicle networks become integral to modern transportation systems, the need for accurate and efficient multi-scene target detection in complex urban environments is increasingly critical. Addressing the challenge of processing and transmitting extensive image data rapidly and accurately under dynamic road conditions, this paper proposes the Multi-scene Target Detection Semantic Communication (MTDSC) method. MTDSC employs convolutional neural networks and region proposal networks for effective target detection in images, and further utilizes spatial pyramid pooling and Long Short-Term Memory (LSTM) networks to assign relevant semantic labels in varied scenarios. These labels are transformed into a transmissible encoding format using a convolutional semantic encoder and a reinforcement learning strategy. The transmission phase leverages variational self-encoders techniques for channel encoding and decoding, ensuring reliable semantic information transfer in complex wireless environments. A crucial aspect of this research is semantic decoding and image reconstruction. To recover original semantic information accurately, a structured Support Vector Machine (SVM) loss function is utilized, designed to capture the structural relationships among labels and enhance the consistency between decoded and original semantic labels. Experimental results show that MTDSC effectively reconstructs semantic images across different road scenes, ensuring key information retention and high-quality image reconstruction, even in low bit rate and complex scenarios.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3378003", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4392939445", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Wan_A_Semantic_Communication_Approach_for_Multiscene_Target_Detection_in_Intelligent_Vehicle_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2026180, "sha256": "dbc364d6b14033957d74bf0f5de77f4c9998be1d442063324a3ee18e75a960be", "ieee_document_id": "10473044", "ieee_document_url": "https://ieeexplore.ieee.org/document/10473044", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10473044", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Adaptive Federated Deep Learning-Based Semantic Communication in the Social Internet of Things", "authors": ["Weixing Tan", "Yusen Wang", "Lei Liu", "Xiaoding Wang", "Tong Chao Ding"], "year": 2024, "publication_date": "2024-10-22", "doi": "10.1109/jiot.2024.3484230", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "34921-34930", "abstract": "The introduction of semantic communication offers an effective solution for achieving efficient and reliable information transmission in the social Internet of Things (SIoT). SIoT combines social networks with the Internet of Things (IoT) to create a “social network of smart objects,” utilizing analytical and statistical models to provide efficient and scalable services. However, ensuring high-quality and reliable data transmission within the SIoT remains a significant challenge. Semantic communication methods can effectively address this issue. Semantic communication represents an advanced paradigm aimed at achieving reliable transmission through semantic-level data compression. In this article, we propose a semantic communication framework based on adaptive federated deep learning. This framework combines source-channel joint coding with channel bandwidth adaptation techniques to enhance transmission efficiency and promote natural and effective information exchange. Specifically, deep reinforcement learning is employed to manage dynamic bandwidth allocation, enabling the selection of optimal bandwidth under varying signal-to-noise ratios and data conditions, thereby improving transmission quality and bandwidth utilization. Additionally, we introduce a training method based on federated learning to enhance the model’s generalization ability under different channel conditions. Simulation results demonstrate that our proposed method outperforms traditional models, exhibiting excellent adaptability to low signal-to-noise ratios and low bandwidth environments, as well as higher stability. This positions our method as a valuable approach for ensuring reliable data communication in the SIoT.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3484230", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4403635731", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Tan_Adaptive_Federated_Deep_Learning_Based_Semantic_Communication_in_the_Social_Internet_of_Things.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1004422, "sha256": "83db5bbd15e91bad6f4bedd24257af4656203eb6dbecdfd2c87a6bf4016c289c", "ieee_document_id": "10729265", "ieee_document_url": "https://ieeexplore.ieee.org/document/10729265", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10729265", "pdf_pages": 10, "pdf_title_token_match": 1.0}
{"title": "Attention-Aware Semantic Communications for Collaborative Inference", "authors": ["Jiwoong Im", "Nayoung Kwon", "Taewoo Park", "Jiheon Woo", "Jaeho Lee", "Yongjune Kim"], "year": 2024, "publication_date": "2024-08-07", "doi": "10.1109/jiot.2024.3440313", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "22", "pages": "37008-37020", "abstract": "We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViT) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions across the entire transformer encoder. Therefore, instead of employing the partitioning strategy, our framework utilizes a lightweight ViT model on the edge device, with the server deploying a complicated ViT model. To enhance communication efficiency and achieve the classification accuracy of the server model, we propose two strategies: 1) attention-aware patch selection and 2) entropy-aware image transmission. Attention-aware patch selection leverages the attention scores generated by the edge device’s transformer encoder to identify and select the image patches critical for classification. This strategy enables the edge device to transmit only the essential patches to the server, significantly improving communication efficiency. Entropy-aware image transmission uses min-entropy as a metric to accurately determine whether to depend on the lightweight model on the edge device or to request the inference from the server model. In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Our experiments demonstrate that the proposed collaborative inference framework can reduce communication overhead by 68% with only a minimal loss in accuracy compared to the server model on the ImageNet dataset.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3440313", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4401387053", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://doi.org/10.1109/jiot.2024.3440313", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2024_Im_Attention_Aware_Semantic_Communications_for_Collaborative_Inference.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6842999, "sha256": "9c3989cf22a68eb3b8fbe352ad6623b6822ecc036a987eafe272f122a0e9dd71", "download_source": "https://arxiv.org/pdf/2404.07217", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "Classification-Driven Discrete Neural Representation Learning for Semantic Communications", "authors": ["Wenhui Hua", "Longhui Xiong", "Sicong Liu", "Lingyu Chen", "Xuemin Hong", "João F. C. Mota", "Xiang Cheng"], "year": 2024, "publication_date": "2024-01-22", "doi": "10.1109/jiot.2024.3354312", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "9", "pages": "16061-16073", "abstract": "Semantic communications is a key enabler of the Internet of Things (IoT). By focusing on the semantic meaning of data rather than bit-level recovery, it allows intelligent agents to communicate necessary information at much lower rates. A promising technique for semantic communications is discrete neural representation learning (DNRL). The main idea is to learn discrete symbols from low-level, high dimensional sensory data, such that each symbol is grounded to a meaningful pattern in the sensory domain. This paper proposes a DNRL scheme that integrates three mechanisms into a coherent framework: contrastive learning, sparse coding, and neural index quantization. The proposed scheme is applied to public image datasets for lossy image compression with a downstream classification task. Results show that the proposed approach produces a highly compact continuous latent representation and a semantic discrete representation, with marginal degradation to the classification accuracy. The interpretability and consistency of the learned sub-symbolic discrete representations are validated by experiments of neural-net dissection, neural-net visualization, and MaxAmp-K classification test, a concept that we propose to evaluate classification performance of extremely compressed signals. Finally, the discrete representations are shown to be useful in rate-adaptive distributed sensing applications at the low-to-medium signal-to-noise ratios (SNR).", "publisher_url": "https://doi.org/10.1109/jiot.2024.3354312", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4391094292", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Hua_Classification_Driven_Discrete_Neural_Representation_Learning_for_Semantic_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2552197, "sha256": "e41e4a8e9a47acd7f7cf28113420dabaa5e9bbee54385853e8a15ce017a1bf36", "ieee_document_id": "10411860", "ieee_document_url": "https://ieeexplore.ieee.org/document/10411860", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10411860", "pdf_pages": 13, "pdf_title_token_match": 0.875}
{"title": "Electric Semantic Compression-Based 6G Wireless Sensing and Communication Integrated Resource Allocation", "authors": ["Haijun Liao", "Jinchao Fan", "Haoyu Ci", "Jiahua Gu", "Zhenyu Zhou", "Bin Liao", "Xiaoyan Wang", "Shahid Mumtaz"], "year": 2024, "publication_date": "2024-09-11", "doi": "10.1109/jiot.2024.3444450", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "24", "pages": "39333-39345", "abstract": "In this article, we address the key problem of sensing and communication integrated resource allocation for 6G-empowered distribution grid hierarchical coordinated control. First, we construct a novel information timeliness metric for electric semantic communication, namely, Peak Age of Semantics (PAoS), which covers the entire lifecycle of information sensing, semantic compression, semantic transmission, and semantic decoding. Second, we propose a sensing and semantic communication integrated resource allocation algorithm based on Top-$\\text {N}^{2}$and hybrid knowledge–statistic-driven fuzzy reinforcement learning. A deep fuzzy neural network is utilized to build a knowledge model between the grid operating state and decision making. The knowledge is embedded into statistic-driven model of reinforcement learning to enhance accuracy of upper confidence bound (UCB) utility evaluation. Finally, simulations based on realistic application scenarios indicate that compared with two comparison algorithms, the proposed algorithm reduces average PAoS by 4.72% and 9.49%, and the maximum PAoS by 5.76% and 13.57%. Additionally, its end-to-end delay trend and semantic packet decoding success rate align more closely with semantic importance.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3444450", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4402435258", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Liao_Electric_Semantic_Compression_Based_6G_Wireless_Sensing_and_Communication_Integrated_Resource_Allocation.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5973332, "sha256": "f438f3ad30960d1148c46a47aa0acb8b5ac117fee4f6697a8c1a3cf8df27d667", "ieee_document_id": "10677448", "ieee_document_url": "https://ieeexplore.ieee.org/document/10677448", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10677448", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Engineering a Lightweight Deep Joint Source-Channel Coding Based Semantic Communication System", "authors": ["Weihan Zhang", "Shaohua Wu", "Siqi Meng", "Jinghang He", "Qinyu Zhang"], "year": 2024, "publication_date": "2024-01-01", "doi": "10.1109/jiot.2024.3463652", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "Deep joint source-channel coding (DeepJSCC) has emerged as a novel technology in semantic communication, coinciding with the increasing demand for the edge devices in the Internet of Things (IoT). Consequently, the deployment of DeepJSCC on edge devices has become a crucial research direction. However, DeepJSCC faces challenges related to channel fading. Moreover, implementing DeepJSCC on the edge devices poses challenges due to the constrained computational resources as well as the compatibility issue between DeepJSCC and digital systems. In this article, we devote to engineering the DeepJSCC system deployed on the edge devices. First, we propose a method named DeepJSCC with Ensemble learning (DeepJSCC-ES) to resist the channel fading. Then, we present a pruning algorithm called the DeepJSCC signal-to-noise ratio (SNR)-adaptive pruning method (DJSAP) to make the DeepJSCC network lightweight, reducing the computational demands on the edge nodes. Further, we propose a method called the simulated fixed-point quantization training based on soft quantization function (SFPQSQ) to tackle the compatibility issue between DeepJSCC and digital systems. Finally, we deploy the whole DeepJSCC system on the edge devices and conduct experiments to test the DeepJSCC system. The results of simulations show that the proposed DeepJSCC-ES system outperforms the baseline DeepJSCC, particularly excelling in low SNR conditions. Furthermore, the parameter size of the pruned model using DJSAP is compressed by 93.37% while the average structural similarity index metric (SSIM) decreases only by 0.92% compared with the baseline DeepJSCC. Additionally, the SFPQSQ works better than the ordinary quantization methods in tackling the compatibility issue between DeepJSCC and digital systems. The experiment results also show that our proposed system can serve as a feasible solution for practical deployment on the edge devices.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3463652", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4402593326", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Deep Joint Source-Channel", "Deep joint source-channel", "DeepJSCC", "semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Zhang_Engineering_a_Lightweight_Deep_Joint_Source_Channel_Coding_Based_Semantic_Communication_System.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6606208, "sha256": "47e510bb0c9491197df0b9340a22a7dd8b96a30cc775cf613efc378b98c9ea64", "ieee_document_id": "10683686", "ieee_document_url": "https://ieeexplore.ieee.org/document/10683686", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10683686", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Explainable Semantic Communication for Text Tasks", "authors": ["Chuanhong Liu", "Caili Guo", "Yang Yang", "Wanli Ni", "Yanquan Zhou", "Lei Li", "Tony Q. S. Quek"], "year": 2024, "publication_date": "2024-08-28", "doi": "10.1109/jiot.2024.3450902", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "24", "pages": "39820-39833", "abstract": "Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this article, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the proposed TESC scheme to two emblematic text tasks: 1) sentiment analysis and 2) question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) the TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency and 2) the TESC scheme enjoys about 150% performance gain compared to the traditional communication method.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3450902", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4401943100", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2024_Liu_Explainable_Semantic_Communication_for_Text_Tasks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 952857, "sha256": "467cc1d16ae15794b4c296fcd66a9f2de8823fa0360e9c83086d194f5fd781ab", "download_source": "https://arxiv.org/pdf/2303.12286v2", "arxiv_id": "2303.12286", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Federated KD-Assisted Image Semantic Communication in IoT Edge Learning", "authors": ["Xinmian Xu", "Yikai Xu", "Haie Dou", "Mingkai Chen", "Lei Wang"], "year": 2024, "publication_date": "2024-07-16", "doi": "10.1109/jiot.2024.3429391", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "21", "pages": "34215-34228", "abstract": "The evolution from fifth-generation mobile communications (5G) to beyond 5G (B5G) will lead to more ubiquitous and smarter paradigms in the Internet of Things (IoT). Communication will shift from the classical information theory to a semantic communication (SemCom) paradigm driven by artificial intelligence (AI) to enhance the capacity and optimize resources. Image SemCom (ISC) will empower IoT applications, such as drone image acquisition. However, ISC requires suitable devices and sufficient computing resources to support complex neural network models, posing a significant challenge. To address this, we propose a federated semantic feature distillation (FedSFD) architecture to improve the global performance of ISC by combining federated learning (FL) and feature distillation (FD) for the feature knowledge transfer. First, lightweight IoT device models in the edge group and the powerful server model alternately minimize losses to update parameters. After learning middle-layer features of all the edge models, the server can guide the individual device models. Second, we incorporate the information bottleneck (IB) concept into the design of loss functions to balance compression and reconstruction. Third, focusing on the tradeoff between the local training and knowledge interaction, FedSFD achieves image semantic reconstruction without sharing the private data, ensuring personalization and privacy protection in the FL framework. Finally, compared to the baseline, the simulation experiments show that the proposed approach achieves better ISC reconstruction and noise robustness during group ISC.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3429391", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4400679606", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Xu_Federated_KD_Assisted_Image_Semantic_Communication_in_IoT_Edge_Learning.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3621054, "sha256": "9b40669d8ce20d58f4eb5f50807b774989438f48a276542cf5fa5db97036a2ac", "ieee_document_id": "10599557", "ieee_document_url": "https://ieeexplore.ieee.org/document/10599557", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10599557", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Goal-Oriented Source Coding and Filtering for Vehicular Communications", "authors": ["José Manuel Giménez-Guzmán", "Israel Leyva‐Mayorga", "Petar Popovski"], "year": 2024, "publication_date": "2024-02-19", "doi": "10.1109/jiot.2024.3367438", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "11", "pages": "19804-19822", "abstract": "Vehicle-to-Everything (V2X) networks will constitute a prominent application in future generations of cellular networks, definitely transforming our conception of transportation systems. A major challenge in V2X networks is the vast amount of data generated by the large number of sensors in the vehicles, which saturates the wireless links. As it is not possible to meet the throughput, timing, and reliability requirements for the total bulk of generated data, one needs to filter out data based on the actual communication goal. In this paper we present an architecture and diverse options to implement filtering and source coding for goal-oriented vehicular communications. We illustrate how filtering and source coding contribute to meeting the strict delay requirements while maintaining energy-efficient operation. Our results show that goal-oriented communications, performed as the combination of Bloom filtering and goal-oriented source coding, can greatly contribute not only to reduce the energy consumption by up to 30% but also to decrease delay, which in turn increases the supported amount of delay-sensitive traffic by up to 819.2%.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3367438", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "deep joint source-channel coding", "joint source-channel coding", "task-oriented communication", "goal-oriented communication", "semantic-aware channel coding"], "openalex_id": "https://openalex.org/W4391929633", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10440149.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10440149.pdf", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["goal-oriented communication", "Goal-Oriented Source Coding", "goal-oriented source coding"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2024_Gimenez_Guzman_Goal_Oriented_Source_Coding_and_Filtering_for_Vehicular_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1470537, "sha256": "709a6ef02e5227b11a277e9b267b4cd453cb0364c03f6b7642df3ebb561cc35b", "download_source": "https://ieeexplore.ieee.org/ielx7/6488907/6702522/10440149.pdf", "pdf_pages": 20, "pdf_title_token_match": 1.0}
{"title": "MDVSC—Efficient Wireless Model Division Video Semantic Communication", "authors": ["Zhicheng Bao", "Haotai Liang", "Chen Dong", "Cong Li", "Xiaodong Xu", "Ping Zhang"], "year": 2024, "publication_date": "2024-09-23", "doi": "10.1109/jiot.2024.3464614", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "2", "pages": "1109-1124", "abstract": "This article introduces a novel method for transmitting video data over noisy wireless channels with high efficiency and controllability. The method derivates from model division multiple access (MDMA) to extract common semantic features from video frames. It also uses deep joint source-channel coding (JSCC) as the main framework to establish communication links and deal with channel noise. An entropy-based semantic importance coding scheme is developed to adjust the data amount accurately and explicitly. We name our method as model division video semantic communication (MDVSC). The main steps of our approach are as follows: first, video frames are transformed into a latent space to reduce computational complexity and redistribute data. Then, common features and individual features are extracted, and semantic importance coding is applied to further eliminate redundant semantic information under the communication bandwidth constraint. We evaluate our method on standard video test sequences and compare it with traditional wireless video coding methods. The results show that MDVSC generally surpasses the conventional methods in terms of quality metrics and has the capability to control code length precisely. Moreover, additional experiments and ablation studies are conducted to demonstrate its potential for various tasks.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3464614", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4402742683", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["deep joint source-channel", "JSCC", "semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2024_Bao_MDVSCEfficient_Wireless_Model_Division_Video_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 13423295, "sha256": "e74a442a56e5bf11983791467aca09ce866aae98b456af1dfcf3231b776fefe9", "pdf_pages": 16, "pdf_title_token_match": 0.875}
{"title": "Mobility-Aware Split-Federated With Transfer Learning for Vehicular Semantic Communication Networks", "authors": ["Guhan Zheng", "Qiang Ni", "Keivan Navaie", "Haris Pervaiz", "Geyong Min", "Aryan Kaushik", "Charilaos C. Zarakovitis"], "year": 2024, "publication_date": "2024-01-30", "doi": "10.1109/jiot.2024.3360230", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "10", "pages": "17237-17248", "abstract": "Machine learning-based semantic communication is a promising enabler for future-generation wireless network systems such as 6G networks. In practice, effective semantic communication requires online training for unknown content. In highly mobile vehicular networks, however, reliable, and efficient model training becomes significantly challenging. The existing distributed learning approaches are also unable to effectively operate in highly dynamic vehicular semantic communication networks. To address these challenges, we propose a novel mobility-aware split-federated with transfer learning (MSFTL) framework based on vehicle task offloading scenarios in this paper. To enable adaptation to the complex vehicle semantic communication, the proposed framework divides the training of the model into four parts and uses the proposed new splitfederated learning. Furthermore, to improve training efficiency, model accuracy, and the ability to adapt in highly mobile environments, we also present a new transfer learning approach integrated into the proposed framework. Particularly, we propose a high-mobility training resource optimisation mechanism based on a Stackelberg game for MSFTL to further reduce training costs and adapt vehicle mobility scenarios. We also investigate the performance of the proposed schemes through extensive simulations. The results validate the proposed approach and indicate its superiority compared to the conventional learning frameworks for semantic communication in vehicular networks.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3360230", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4391341749", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://repository.essex.ac.uk/38208/1/Author_accepted_final_version.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2024_Zheng_Mobility_Aware_Split_Federated_With_Transfer_Learning_for_Vehicular_Semantic_Communication_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3356549, "sha256": "e02a318463df31888979b74b5646866a42110761411d953834f9e1be0e5d7bb4", "download_source": "https://repository.essex.ac.uk/38208/1/Author_accepted_final_version.pdf", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "Multilevel Feature Transmission in Dynamic Channels: A Semantic Knowledge Base and Deep-Reinforcement-Learning-Enabled Approach", "authors": ["Xiangyu Gao", "Hao Yin", "Yaping Sun", "Dongyu Wei", "Xiaodong Xu", "Hao Chen", "Wen Rong Wu", "Shuguang Robert Cui"], "year": 2024, "publication_date": "2024-12-04", "doi": "10.1109/jiot.2024.3511592", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "8", "pages": "10150-10162", "abstract": "With the proliferation of edge computing, efficient artificial intelligence inference on edge devices has become essential for intelligent applications, such as autonomous vehicles and virtual/augmented reality. In this context, we address the problem of efficient remote object recognition by optimizing feature transmission between mobile devices and edge servers. We propose an optimization framework to tackle the challenges posed by dynamic channel conditions and device mobility in end-to-end communication systems. Our approach builds upon existing methods by leveraging a semantic knowledge base to drive multilevel feature transmission, accounting for temporal factors, state transitions, and dynamic elements throughout the transmission process. Additionally, we enhance the multilevel feature transmission policy by introducing an additional fifth-level edge-assisted semantic communication, which maximizes recognition performance by leveraging a large semantic knowledge base on the edge server. Formulated as an online optimization problem, our framework aims to simultaneously minimize semantic loss and adhere to specified transmission latency thresholds. To achieve this, we design a soft actor-critic-based deep reinforcement learning system with a carefully designed reward structure for real-time decision making. This approach overcomes the optimization difficulty of the NP-hard problem while fulfilling the optimization objectives. Numerical results showcase the superiority of our approach compared to traditional greedy methods across various system setups using open-source datasets.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3511592", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4405022613", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Gao_Multilevel_Feature_Transmission_in_Dynamic_Channels_A_Semantic_Knowledge_Base_and_Deep_Reinforcement_Learning_En.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4859522, "sha256": "b6b0a5df2695fcbf2e338d08b10dd3dc83e94e022d8b7362b64168da98d882f1", "ieee_document_id": "10778255", "ieee_document_url": "https://ieeexplore.ieee.org/document/10778255", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10778255", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Multimodal Virtual Semantic Communication for Tiny-Machine-Learning-Based UAV Task Execution", "authors": ["Chao Ren", "Zongrui He", "Yin Long", "Chuan Zhen Zhao", "Lei Sun", "Kexin Xu"], "year": 2024, "publication_date": "2024-06-18", "doi": "10.1109/jiot.2024.3416253", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "19", "pages": "30864-30874", "abstract": "In the 6G integrated air-ground network, the process of accomplishing complex tasks through the integrated multimodal communication faces challenges induced by unmanned aerial vehicles (UAVs), such as limited communication, storage and computing capabilities, and the existence of heterogeneous UAV multimodal information and carriers. Inspired by the process of semantic communication, we view successful execution of advanced UAV tasks as semantic recognition and pragmatic execution. Tiny machine learning (TinyML) provides the UAV advanced algorithms and models that can be run on the low-power and resource-constrained platforms. In this article, from the perspective of semantic communication and leveraging the applicability of TinyML for UAVs, we map the heterogeneous multimodal communication and UAV task execution processes aiming to better utilize the capabilities of machine learning and semantic communication to enhance the pragmatic task execution of UAVs. Multimodal virtual semantic communication can provide task-related auxiliary information, enabling the complementary integration of multiple independent modalities in the task domain. The proposed scheme and model achieve a deep integration of communication, sensation, and computation ultimately enhancing the practical task execution capability of UAVs.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3416253", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "task-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4399767886", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Ren_Multimodal_Virtual_Semantic_Communication_for_Tiny_Machine_Learning_Based_UAV_Task_Execution.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1455217, "sha256": "ebf6f654e868a377685a0c543e6a25fd9b3b6c1ef8ddb5a21f04f901bd64a984", "ieee_document_id": "10560513", "ieee_document_url": "https://ieeexplore.ieee.org/document/10560513", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10560513", "pdf_pages": 11, "pdf_title_token_match": 1.0}
{"title": "Rate-Adaptable Multitask-Oriented Semantic Communication: An Extended Rate–Distortion Theory-Based Scheme", "authors": ["Fangfang Liu", "Zhengfen Sun", "Yang Yang", "Caili Guo", "Shan Zhao"], "year": 2024, "publication_date": "2024-01-08", "doi": "10.1109/jiot.2024.3350656", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "9", "pages": "15557-15570", "abstract": "Semantic communication, as a new paradigm for next-generation communication, aims to transmit semantic symbols for artificial intelligence (AI) tasks. Existing research typically requires extracting and transmitting specialized semantics for each AI task when multiple target AI tasks exist. Considering that each AI task may share common semantics, this article proposes a joint source–channel coding scheme for a multitask semantic communication (MTSC) system, which can extract the common semantics required by multiple AI tasks, and thus reduce the overall semantic transmission. To this end, we first formulate the MTSC problem as a rate–distortion problem that simultaneously considers the rate of extracted semantics and the distortion of multiple AI tasks. Then, we derive a new form of rate–distortion, called extended rate–distortion, which can guide the compact semantics extraction of multiple AI tasks simultaneously. Additionally, we derive a self-consistent equation for this extended rate–distortion form, theoretically proving the effectiveness of this approach. To ensure proper tradeoff between the rates and distortions of multiple AI tasks, we further propose a rate adjustment module that can dynamically adjust the rate according to channel conditions. We validate our experimental results on multiple data sets, which show that the proposed method can reduce transmission overhead by 40%–50% and achieve a 7.6% improvement in multitask performance.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3350656", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "task-oriented communication", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4390660246", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Liu_Rate_Adaptable_Multitask_Oriented_Semantic_Communication_An_Extended_RateDistortion_Theory_Based_Scheme.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1563620, "sha256": "29803e426b89976804dd8247ab0945b7ef7ea63fe04f4c8f50a6dba1255e707e", "ieee_document_id": "10382545", "ieee_document_url": "https://ieeexplore.ieee.org/document/10382545", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10382545", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Semantic Communication Meets Edge Intelligence: Semantic-Relay-Aided Text Transmissions", "authors": ["Zeyang Hu", "Changsheng You", "Tianyu Liu", "Dingzhu Wen", "Ye Hu", "Yuanhao Cui", "Yi Gong", "Kaibin Huang"], "year": 2024, "publication_date": "2024-08-16", "doi": "10.1109/jiot.2024.3433431", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "24", "pages": "39409-39423", "abstract": "Semantic communication (SemCom) has emerged as a promising technology to improve the spectrum efficiency of next-generation wireless networks, by extracting meaningful content from the data and transmitting relevant semantic information only. However, the existing research usually overlooks the limited computing and storage resources on the mobile devices, which may make it unaffordable to implement resource-demanding deep learning (DL)-based semantic encoders/decoders. Moreover, besides the end-to-end SemCom framework, cooperative SemCom has not been well studied in the existing works, which can further enhance the communication performance. To address these issues, we propose a new architecture in this article, called semantic relay (SemRelay), which acts as an edge server to provide DL-enabled SemCom (DeepSC) services for two categories of edge users, called semantic users (SemUsers) with rich computing resources and conventional users (ConUsers) with limited resources. Two new transmission protocols are proposed for enabling text transmissions from the base station to the SemUsers and ConUsers, respectively, via the SemRelay (edge server). Moreover, an optimization problem is formulated to jointly design the SemRelay transmit power allocation and system bandwidth allocation to maximize the weighted sum-rate of all the users. Although this problem is nonconvex and hence difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution by applying the block coordinate descent and successive convex approximation techniques. Finally, the numerical results demonstrate the effectiveness of our proposed algorithm and the superior performance of the proposed SemRelay as compared to the traditional decode-and-forward relays, especially in the small bandwidth regime.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3433431", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4401634165", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication", "Semantic Communication", "semantic relay", "Semantic-Relay"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Hu_Semantic_Communication_Meets_Edge_Intelligence_Semantic_Relay_Aided_Text_Transmissions.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2182415, "sha256": "361d4606005c04eae6af58956735c2bf4ea9c583ea26bea35e3b67ab1757f542", "ieee_document_id": "10638143", "ieee_document_url": "https://ieeexplore.ieee.org/document/10638143", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10638143", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Semantic Similarity Score for Measuring Visual Similarity at Semantic Level", "authors": ["Senran Fan", "Zhicheng Bao", "Chen Dong", "Haotai Liang", "Xiaodong Xu", "Ping Zhang"], "year": 2024, "publication_date": "2024-12-16", "doi": "10.1109/jiot.2024.3518543", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "9", "pages": "12034-12047", "abstract": "With the rapid development of Internet of Things (IoT) technology, more sensors are required to operate in complex channel scenarios and under limited communication resources. Semantic communication, as an emerging paradigm, extracts, transmits, and reconstructs information at the semantic level, offering advantages, such as high compression rates and strong noise resistance. These features are expected to find widespread application across various IoT scenarios. However, widely used image similarity evaluation metrics like peak signal-to-noise ratio and multiscale structural similarity index primarily focus on pixel or structural features, making it challenging to accurately measure the loss of semantic-level information during transmission. This limitation poses challenges for the performance evaluation of visual semantic communication systems and restricts the emergence of more novel and efficient systems. To address this issue, we propose a new semantic evaluation metric-semantic similarity score (SeSS). This metric is based on Scene Graph Generation and graph matching techniques, transforming image similarity scores into graph matching scores. By manually annotating thousands of image pairs, we fine-tuned the hyperparameters within SeSS to align it more closely with human semantic perception. The performance of SeSS has been tested across various image datasets and specific IoT visual tasks. Experimental results demonstrate the effectiveness of SeSS in measuring differences in semantic-level information between images, making it a valuable tool for evaluating visual semantic communication systems. This development is expected to encourage the emergence of more robust systems suited for diverse IoT scenarios. The code of SeSS is openly available onhttps://github.com/FSR3340/Semantic_Similarty_ScoreGitHub.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3518543", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4405429592", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2024_Fan_Semantic_Similarity_Score_for_Measuring_Visual_Similarity_at_Semantic_Level.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 10865926, "sha256": "59d2634100fa71a74c50c63b10b00c726780e869746a2324b835ab657a6e18c4", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Semantic-Aware Architecture Design for a Lifelong Swarm Metaverse", "authors": ["Ning Wang", "Yinxuan Wu", "Beatriz Lorenzo", "Bing Liu"], "year": 2024, "publication_date": "2024-12-19", "doi": "10.1109/jiot.2024.3520518", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "9", "pages": "12468-12482", "abstract": "As the Metaverse evolves with developments in AI, semantic communication, edge computing, and blockchain, it encounters challenges in adapting to dynamic environments and meeting rising communication and computation needs. In this article, we propose a semantic-aware UAV-based architecture tailored to the dynamic Metaverse that leverages UAV swarms consisting of a collection UAV and edge UAV servers. By mapping different semantic features of Metaverse environments, such as amount of tasks, arrival rate, throughput, and latency requirements, we jointly optimize the mobility, task allocation, and resource allocation in a dynamic Metaverse system. First, a particle swarm optimization-based collection-edge mobility algorithm (PSO-CEMA) is designed to optimize the mobility of UAV servers. Second, to facilitate timely and stable task allocation with reduced complexity, we propose a dual-queue system and a Lyapunov drift function-based dynamic programming task allocation algorithm (LDF-DPTAA). Then, we adopt lifelong learning and design a collection-edge joint training and processing algorithm (LL-CJTPA) to optimize the dynamic allocation of computational resources in the swarm. Finally, we integrate our algorithms into a PSO-LDF-LL algorithm to serve the dynamic Metaverse system. Simulation results show that our approach effectively optimizes UAV servers’ positions and task allocation, significantly reduces the training time when facing new tasks, and enhances the stability and efficiency of the network in dynamic settings while reducing congestion.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3520518", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4405600038", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Wang_Semantic_Aware_Architecture_Design_for_a_Lifelong_Swarm_Metaverse.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5703707, "sha256": "7b0235b741e00f4d155ec0a8e349505d7ade7e4e472ab94daeca9a1ba527d6ae", "ieee_document_id": "10807275", "ieee_document_url": "https://ieeexplore.ieee.org/document/10807275", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10807275", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning", "authors": ["Zhiyu Shao", "Qiong Wu", "Pingyi Fan", "Nan Cheng", "Wen Chen", "Jiangzhou Wang", "Khaled B. Letaief"], "year": 2024, "publication_date": "2024-08-23", "doi": "10.1109/jiot.2024.3448538", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "23", "pages": "38521-38536", "abstract": "This article investigates semantic communication in high-speed mobile Internet of Vehicles (IoV), focusing on spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We propose a semantic-aware spectrum-sharing (SSS) algorithm using deep reinforcement learning (DRL) with a soft actor-critic (SAC) approach. We start with semantic information extraction, redefining metrics for V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). We then apply the SAC algorithm to optimize decisions V2V and V2I spectrum-sharing decisions on semantic information. This optimization aims to maximize HSSE and enhance the success rate of effective semantic information transmission (SRS), including determining the optimal V2V and V2I sharing strategies, transmission power, and the length of transmitted semantic symbols. Experimental results show that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum-sharing algorithms and spectrum-sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3448538", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4401806244", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "semantic transmission", "semantic-aware spectrum", "Semantic-Aware Spectrum"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2024_Shao_Semantic_Aware_Spectrum_Sharing_in_Internet_of_Vehicles_Based_on_Deep_Reinforcement_Learning.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1646430, "sha256": "b70ffcb135963c3ad0642d97af88ba4881dad0a29605edf1c578bae06f37e937", "download_source": "https://arxiv.org/pdf/2406.07213v4", "arxiv_id": "2406.07213", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Source Value-Based Resource Allocation in Task-Oriented Communications", "authors": ["Xiaoyu Chi", "Shujun Han", "Xiaodong Xu", "Lin Li", "Hui Wang", "Xiaoqi Qin", "Liang Jin", "Ping Zhang"], "year": 2024, "publication_date": "2024-07-19", "doi": "10.1109/jiot.2024.3430905", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "24", "pages": "39395-39408", "abstract": "With the explosive growth of communication requirements for real-time intelligent tasks, mobile communication is shifting from the traditional communication to task-oriented communication, where the transmitted data is shifting from undifferentiated transmission to value-oriented transmission. To maximize the value of transmitted data, it is urgent to match the source decisions with the task demands and wireless channel state. In this article, we focus on the joint source-channel optimization problem in task-oriented communication, and we design the timeliness-accuracy degradation (TAD) metric to measure the value of transmitted source. Moreover, we design a source value-based resource allocation scheme to minimize the TAD through joint optimization of task data generation and compression strategies, bandwidth allocation, and transmit power selection. Furthermore, to avoid the curse of dimensionality, we propose dimension-refined reinforcement learning (DRRL) algorithm to obtain the optimal solution of the problem in a stable and low-complexity manner. Numerical results demonstrate that the designed scheme can effectively improve the task performance and verify the low complexity and stability of the algorithm.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3430905", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "task-oriented communication", "goal-oriented communication"], "openalex_id": "https://openalex.org/W4400810104", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["task-oriented communication", "Task-Oriented Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2024_Chi_Source_Value_Based_Resource_Allocation_in_Task_Oriented_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6304180, "sha256": "a231bf5dfb29b3e17af7e7e93e1adaa835e9c6f33f79cdac0727f28bbf9994bd", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "UAV-Assisted Digital-Twin Synchronization With Tiny-Machine-Learning-Based Semantic Communications", "authors": ["Jianhang Tang", "Jiangtian Nie", "Jingpan Bai", "Ji Hui Xu", "Shaobo Li", "Yang Zhang", "Yanli Yuan"], "year": 2024, "publication_date": "2024-05-15", "doi": "10.1109/jiot.2024.3401229", "venue": "IEEE Internet of Things Journal", "volume": "11", "issue": "17", "pages": "28437-28451", "abstract": "Semantic communication is an emerging paradigm for digital twin (DT) synchronization in unmanned aerial vehicle (UAV)-assisted edge computing environments, where machine learning (ML) models are deployed on edge servers and UAVs as semantic encoders and decoders to perform real-time synchronization. However, with limited system resources, additional computation workloads are still brought to all participants for semantic information extraction and recovery. In this work, we propose an optimized tiny ML-based DT synchronization framework to minimize the synchronization latency in UAV-assisted edge computing environments, considering time-average constraints on virtual energy deficit queue stability. Due to the coexistence of tiny ML-based semantic communications, a semantic extraction factor is introduced to formulate the DT synchronization problem as a time-average time minimization problem. By leveraging the Lyapunov optimization framework, the multi-stage DT synchronization problem is transformed into several per-slot resource allocation problems. To solve the per-slot optimization problem efficiently, a deep reinforcement learning-based synchronization (DRLS) algorithm is proposed, where an actor-critic structure is adopted to generate synchronization actions with low time complexity. Finally, we conduct simulation experiments to evaluate the performance of the proposed DRLS scheme. Numerical results demonstrate that our DRLS algorithm can reduce 8.23% of DT synchronization delay and 15.31% of synchronization data dropping rates on average by comparing it with the UAV-edge collaborative synchronization scheme without semantic communications. Besides, the DRLS algorithm can achieve up to 57.14% synchronization energy reduction compared with representative synchronization policies.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3401229", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4396941391", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Tang_UAV_Assisted_Digital_Twin_Synchronization_With_Tiny_Machine_Learning_Based_Semantic_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1507140, "sha256": "903474176ff190b3e961bf0fe55816699ea02983196e5798d1af7372f4f8d061", "ieee_document_id": "10530992", "ieee_document_url": "https://ieeexplore.ieee.org/document/10530992", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10530992", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Wireless Power Transfer Meets Semantic Communication For Resource-Constrained IoT Networks: A Joint Transmission Mode Selection and Resource Management Approach", "authors": ["Nguyen Huu Sang", "Nguyen Duc Hai", "Nguyen Duc Duy Anh", "Nguyen Cong Luong", "Van‐Dinh Nguyen", "Shimin Gong", "Dusit Tao Niyato", "Dong In Kim"], "year": 2024, "publication_date": "2024-01-01", "doi": "10.1109/jiot.2024.3464646", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "In this work, we consider the integration of energy harvesting (EH) and semantic communication strategies in resource-constrained Internet of Things (IoT) systems. The system empowers IoT devices to harvest energy from a base station, utilizing this harvested energy for the extraction and transmission of semantic information (e.g., scene graphs). To maximize the total transmission of image data or scene graphs to the central station, we formulate a comprehensive problem that jointly optimizes the EH duration, original image selection, transmit power, and channel allocation to IoT devices. The challenges arising from the dynamic environments and uncertain system parameters are effectively tackled by policy-based deep reinforcement learning algorithms, i.e., advantage actor-critic (A2C) and proximal policy optimization (PPO). Simulation results are implemented on the real data set clearly showing the superior performance achieved by our proposed algorithms compared to the baseline schemes. Notably, our approach enables IoT devices to transmit a greater number of original images and scene graphs with increased triplets to the central station, as highlighted in the simulation outcomes. This phenomenon showcases the potential of our strategy to enhance the capabilities of IoT systems in dynamic environments.", "publisher_url": "https://doi.org/10.1109/jiot.2024.3464646", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "wireless video transmission"], "openalex_id": "https://openalex.org/W4402673517", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2024_Sang_Wireless_Power_Transfer_Meets_Semantic_Communication_For_Resource_Constrained_IoT_Networks_A_Joint_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3119854, "sha256": "ba19eb873a33d9a407ef50702dbf1d437b337df0a16bf9271727f52f77166ae1", "ieee_document_id": "10684702", "ieee_document_url": "https://ieeexplore.ieee.org/document/10684702", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10684702", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "A Hybrid Federated Learning Framework for Task-Oriented Semantic Communication", "authors": ["Haofeng Sun", "Wanli Ni", "Hui Tian", "Jingheng Zheng", "Gaofeng Nie", "Dusit Tao Niyato"], "year": 2025, "publication_date": "2025-03-22", "doi": "10.1109/jiot.2025.3553504", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "13", "pages": "23444-23461", "abstract": "In existing deep learning-based semantic communication systems, centralized training of semantic models brings a risk of privacy leakage, whereas distributed training imposes a huge computational burden on user equipments (UEs). To address these challenges, we propose a hybrid federated learning (Hybrid-FL) framework to alleviate the computational burden on UEs while protecting the user privacy. Specifically, each UE uploads local gradients and semantic symbols to the base station for the collaborative training of global and local semantic models. Furthermore, we propose a joint communication and computation scheme for supporting the model aggregation and semantics transmission. To gain deep insights, we expose the joint impact of communication and computation on the convergence behavior of Hybrid-FL by deriving an upper bound. Then, we formulate a mixed-integer nonlinear programming problem to improve the convergence performance of Hybrid-FL, which is then effectively solved by using our proposed algorithm that developed based on alternating and matching theory. Experimental results demonstrate that Hybrid-FL outperforms conventional FL by achieving a 20% accuracy gain and a 80% latency reduction.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3553504", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "task-oriented communication", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4408710292", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Sun_A_Hybrid_Federated_Learning_Framework_for_Task_Oriented_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2290515, "sha256": "36d3b2ca097de8f96574c4a33153970c1fb7a0713abb10c4fb9298005a997899", "ieee_document_id": "10937229", "ieee_document_url": "https://ieeexplore.ieee.org/document/10937229", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10937229", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "A Novel Lightweight Joint Source-Channel Coding Design in Semantic Communications", "authors": ["Xianhua Yu", "Dong Li", "Ning Zhang", "Xuemin Shen"], "year": 2025, "publication_date": "2025-04-01", "doi": "10.1109/jiot.2025.3556909", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "11", "pages": "18447-18450", "abstract": "Semantic communication has emerged as a promising solution to meet the growing demand for efficient data transmission in the information age. Unlike traditional communication methods that focus on transmitting raw data, semantic communication prioritizes preserving the meaning of transmitted information, which significantly reduces the data volume. However, implementing semantic communication systems in resource-constrained environments, such as Internet of Things (IoT) devices, remains challenging due to limited computational resources. In this letter, we propose a novel lightweight deep learning (DL) model, termed the lightweight image compression and reconstruction network (LICRnet). LICRnet leverages depthwise separable convolution (DSC) and a local and nonlocal mixture (LNLM) block to significantly reduce computational costs. Additionally, the LNLM incorporates a variable window size-based multiscale attention mechanism (VW-MSA), enabling it to effectively learn from both local detailed features and global high-level meaningful features. Extensive simulations demonstrate that LICRnet significantly reduces computational complexity while maintaining satisfactory image compression and reconstruction performance, making it highly suitable for deployment in resource-constrained environments.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3556909", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4409049934", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Joint Source-Channel Coding", "semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Yu_A_Novel_Lightweight_Joint_Source_Channel_Coding_Design_in_Semantic_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 533218, "sha256": "2cda278eb0c19a8e77d54f4500a79659aa26c7b675eaa385f5619563a5ee3c03", "ieee_document_id": "10947172", "ieee_document_url": "https://ieeexplore.ieee.org/document/10947172", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10947172", "pdf_pages": 4, "pdf_title_token_match": 1.0}
{"title": "A Semantic Approach to Successive Interference Cancellation for Multiple Access Networks", "authors": ["Mingxiao Li", "Kaiming Shen", "Shuguang Robert Cui"], "year": 2025, "publication_date": "2025-01-21", "doi": "10.1109/jiot.2025.3532484", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "11", "pages": "16424-16437", "abstract": "Differing from the conventional communication system paradigm that models information source as a sequence of (i.i.d. or stationary) random variables, the semantic approach aims at extracting and sending the high-level features of the content deeply contained in the source, thereby breaking the performance limits from the statistical information theory. As a pioneering work in this area, the deep learning-enabled semantic communication (DeepSC) constitutes a novel algorithmic framework based on the transformer—which is a deep learning tool widely used to process text numerically. The main goal of this work is to extend the DeepSC approach from the point-to-point link to the multiuser multiple access channel (MAC). The interuser interference has long been identified as the bottleneck of the MAC. In the classic information theory, the successive interference cancellation (SIC) scheme is a common way to mitigate interference and achieve the channel capacity. Our main contribution is to incorporate the SIC scheme into the DeepSC. As opposed to the traditional SIC that removes interference in the digital symbol domain, the proposed semantic SIC works in the domain of the semantic word embedding vectors. Furthermore, to enhance the training efficiency, we propose a pretraining scheme and a partial retraining scheme that quickly adjust the neural network parameters when new users are added to the MAC. We also modify the existing loss function to facilitate training. Finally, we present numerical experiments to demonstrate the advantage of the proposed semantic approach as compared to the existing benchmark methods.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3532484", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4406657818", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "medium", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2025_Li_A_Semantic_Approach_to_Successive_Interference_Cancellation_for_Multiple_Access_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 1265518, "sha256": "b93edb2d71612b11b0392994a7f63ddb8f3181c1e9938c158e2638ef65c93f6a", "download_source": "https://arxiv.org/pdf/2501.10926v1", "arxiv_id": "2501.10926", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "A Universal Speech Semantic Communication Framework for Multitask Applications Based on Unsupervised Models", "authors": ["Haiyan Wang", "Zan Li", "Xiaohui Zhao", "Zheng Chang", "Fengye Hu"], "year": 2025, "publication_date": "2025-09-18", "doi": "10.1109/jiot.2025.3611506", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "23", "pages": "50642-50652", "abstract": "With the increasing complexity of next generation network applications and the coexistence of diverse service requirements, Generative AI (GAI) and Large Models (LMs) based semantic communication are widely regarded as promising solutions to address these challenges. The goal of these systems is not only to reduce system burden by reducing transmission data, but also to adapt to new and complex requirements. In this paper, we propose a semantic communication system designed to meet diverse requirements of speech applications while enabling accurate speech transmission. The semantic encoder comprises an unsupervised model wav2vec 2.0 for learning universal speech representations to enable adaptability across various speech-related tasks. It also includes a prosodic feature encoder from the style embedding module of Global Style Tokens (GST) Tacotron. The semantic decoder integrates a phoneme recognition module and a GST-Tacotron-based text-to-speech (TTS) module to facilitate accurate and expressive reconstruction of the original speech signal, with the incorporation of prosodic features enhancing the naturalness and intelligibility of the synthesized speech. The proposed system has been tested in noisy channels. It demonstrates that the system maintains superior and robust performance even at Bit Error Rate (BER) of 10−1, as reflected by a stable Character Error Rate (CER) approximately 0.0940 and 0.0649 for the base and large versions of wav2vec 2.0 respectively in speech recognition, and consistent cFDSD scores approximately 0.9 in speech quality assessment. This performance surpasses that of the existing semantic communication systems, while also providing reliable support for a wider range of downstream speech applications.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3611506", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414322230", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Wang_A_Universal_Speech_Semantic_Communication_Framework_for_Multitask_Applications_Based_on_Unsupervised_Models.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5003676, "sha256": "6fcb3dab9a1d08830391e4ee23a69b24d4a29852bde66730e8ef71b2819e6a8b", "ieee_document_id": "11171613", "ieee_document_url": "https://ieeexplore.ieee.org/document/11171613", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11171613", "pdf_pages": 11, "pdf_title_token_match": 1.0}
{"title": "Advanced Semantic Communication Techniques for IoT Using Disentangled Information Bottleneck", "authors": ["Wenwu Xie", "Ming Xiong", "Liang Yang", "Ji Wang", "Xingwang Li", "Zhihe Yang"], "year": 2025, "publication_date": "2025-02-17", "doi": "10.1109/jiot.2025.3542753", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "12", "pages": "20122-20137", "abstract": "This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier$\\beta $in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize$\\beta $several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3542753", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4407637780", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic coding", "semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Xie_Advanced_Semantic_Communication_Techniques_for_IoT_Using_Disentangled_Information_Bottleneck.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 14612085, "sha256": "9acd928e16b7787fce7ee1d1b13ebb40cb72e1b17e890ed85e32e81ed90d0c13", "ieee_document_id": "10891449", "ieee_document_url": "https://ieeexplore.ieee.org/document/10891449", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10891449", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "An N -ary-Based Reliable Semantic Communication System With a Correction and Reasoning Approach", "authors": ["Chenlin Xing", "Jie Lv", "Tao Luo", "Xinxin He", "Zhilong Zhang"], "year": 2025, "publication_date": "2025-05-20", "doi": "10.1109/jiot.2025.3571962", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "15", "pages": "30794-30807", "abstract": "Semantic communication systems enhance communication efficiency and task performance by understanding the semantics of transmitted information, offering broad application prospects. Nevertheless, existing semantic communication systems struggle to handle information with complex relational semantics. Motivated by this limitation, this paper proposes the n-ary relation semantic communication system with correction and reasoning (NRSC-CR). In NRSC-CR, semantic information is extracted from transmitted texts into n-ary relations. To enhance robustness against wireless channel influence, a correction algorithm based on the rationality of primary triplets and auxiliary information pairs (CPA) is introduced at the receiver. Furthermore, by deeply learning the interactions within and between n-ary relations, a reasoning algorithm based on global and local dependency (RGL) is proposed to address the performance degradation caused by excessive influence under low signal-to-noise ratio (SNR) conditions. Extensive simulation results show that the proposed NRSC-CR has advantages in understanding complex relational semantics, channel robustness, and data transmission efficiency.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3571962", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4410536995", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Xing_An_N_ary_Based_Reliable_Semantic_Communication_System_With_a_Correction_and_Reasoning_Approach.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3686471, "sha256": "2025b49b0d22f491308f252d3e94aaab06d6ba9199b4309a367d498c0e625b65", "ieee_document_id": "11007553", "ieee_document_url": "https://ieeexplore.ieee.org/document/11007553", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11007553", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Cross-Layer Encrypted Semantic Communication Framework for Panoramic Video Transmission", "authors": ["Haixiao Gao", "Mengying Sun", "Xiaodong Xu", "Bingxuan Xu", "Shujun Han", "Bizhu Wang", "Sheng Jiang", "Chen Dong", "Ping Zhang"], "year": 2025, "publication_date": "2025-07-25", "doi": "10.1109/jiot.2025.3592181", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "19", "pages": "40545-40562", "abstract": "Compatibility between semantic communication and traditional mobile communication systems remains a significant challenge. Therefore, we propose a cross-layer encrypted semantic communication (CLESC) framework for panoramic video transmission, incorporating feature extraction, encoding, encryption, cyclic redundancy check (CRC), and retransmission processes to achieve compatibility between semantic communication and traditional communication systems. Additionally, we propose an adaptive cross-layer transmission mechanism that dynamically adjusts CRC, channel coding, and retransmission schemes based on the importance of semantic information. This mechanism ensures that important information is prioritized under poor transmission conditions. To verify the aforementioned framework, we design an end-to-end adaptive panoramic video semantic transmission (APVST) network that leverages a deep joint source-channel coding (JSCC) structure and attention mechanism, integrated with a latitude adaptive module that facilitates adaptive semantic feature extraction and variable-length encoding of panoramic videos. Simulation results demonstrate that the proposed CLESC framework effectively achieves compatibility and adaptability between semantic and traditional communication systems, significantly enhancing channel robustness. Compared to traditional and artificial intelligence (AI)-based video source coding transmission schemes, our proposed CLESC achieves superior transmission performance under low signal-to-noise ratio (SNR) conditions.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3592181", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4412719040", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["deep joint source-channel", "JSCC", "semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Gao_Cross_Layer_Encrypted_Semantic_Communication_Framework_for_Panoramic_Video_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6566919, "sha256": "80b98c9db23c110e8f29525dd0d6eff09e6564640f711dcf1d56452f83638fd5", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "Deep Semantic Communication for Knowledge Sharing in Internet of Vehicles", "authors": ["Zhihong Wang", "Supeng Leng", "Hanwen Zhang", "Chau Yuen"], "year": 2025, "publication_date": "2025-01-20", "doi": "10.1109/jiot.2025.3531910", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "12", "pages": "19202-19214", "abstract": "Along with the development of intelligent transportation system (ITS), artificial intelligence (AI)-based machine learning technologies have been widely utilized in Internet of Vehicles (IoV). Neural network (NN)-based knowledge sharing among vehicles and road side units (RSUs) presents considerable benefits for enhancing vehicle intelligence. However, it is challenging to ensure the efficiency of knowledge sharing under unstable connectivity among vehicles with different NN model architectures. In this article, we propose a new deep semantic communication framework for knowledge sharing (SCKS), enabling one-to-many NN model transmission and realizing efficient knowledge sharing in an IoV. Based on this framework, a generative distillation algorithm is designed to extract the semantic features of NN model, which can ensure the efficiency of the transmitter for knowledge sharing across different NN models and reduce communication bandwidth demand. In order to facilitate an effective understanding of semantic information by heterogeneous receivers, we design a generative adversarial networks (GAN)-based semantic decoding algorithm. Numerical results on CIFAR10 and ImageNet datasets show that the proposed SCKS outperforms the baseline, especially in the low-signal-to-noise (SNR) region. In particular, the simulation results demonstrate superiority of proposed SCKS scheme in terms of bandwidth requirements and computational efficiency for knowledge sharing cross different NN architectures than the state-of-art scheme, including DeepJSCC and knowledge distillation (KD).", "publisher_url": "https://doi.org/10.1109/jiot.2025.3531910", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4406610673", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["DeepJSCC", "semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Wang_Deep_Semantic_Communication_for_Knowledge_Sharing_in_Internet_of_Vehicles.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2300954, "sha256": "a4ac3a1549a13cf0eec8533df45db69909490cf6454f5a6cc03b04a29142ca28", "ieee_document_id": "10847825", "ieee_document_url": "https://ieeexplore.ieee.org/document/10847825", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10847825", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Diffusion-Based Semantic-Communication-Assisted Low-Altitude Intelligent Service for IoT", "authors": ["Fan Jian", "Pengfei Ren", "Jianrui Chen", "Junhui Qian", "Jingjing Wang", "Chunxiao Jiang"], "year": 2025, "publication_date": "2025-01-16", "doi": "10.1109/jiot.2025.3530462", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "10", "pages": "13568-13580", "abstract": "Autonomous aerial vehicles (AAVs), as the key Internet of Things (IoT) devices, play a dominant position in low-altitude environments. Semantic communication (SC), as the next-generation communication technology, serves as a bridge for surpassing the Shannon limit toward the 6G wireless network. Establishing air-ground SC to provide intelligent IoT services is a crucial initiative for building future smart cities. In this article, we propose an AAV-based SC framework, named diffusion joint source-channel coding (D-JSCC). Abandoning traditional convolutional neural networks, we use transformers as the backbone and innovatively incorporate the diffusion model (DM) for image enhancement, achieving an optimal balance between image distortion and human perception. To accurately capture the numerical and perceptual loss induced by wireless channels and seamlessly amalgamate the DM with SC, we integrate channel states as strong prior information to refine the sampling process. Furthermore, we employ the gradient guidance strategy, which counteracts the randomness of sampling ensuring high robustness in harsh communication conditions. Additionally, we strike a balance between performance and sampling steps, ensuring both efficient computation and high-quality image enhancement. Comprehensive experiments demonstrate the advantages of D-JSCC across different communication environments.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3530462", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4406457456", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["joint source-channel coding", "JSCC", "Semantic communication", "Semantic-Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Jian_Diffusion_Based_Semantic_Communication_Assisted_Low_Altitude_Intelligent_Service_for_IoT.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3155645, "sha256": "de40ade0696837ab9559f950eb80a0ec07b18d485a3c649e734fbd4967a356ed", "ieee_document_id": "10843801", "ieee_document_url": "https://ieeexplore.ieee.org/document/10843801", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10843801", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Doppler-Adaptive Digital Semantic Communication for Low Earth Orbit Satellite Systems", "authors": ["Joonho Seon", "Seongwoo Lee", "Soo Hyun Kim", "Young Ghyu Sun", "Hyowoon Seo", "Dong In Kim", "Jin Young Kim"], "year": 2025, "doi": "10.1109/jiot.2025.3614234", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "24", "pages": "52900-52912", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2025.3614234", "sources": ["Crossref keyword search"], "matched_queries": ["semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "record_type": "journal-article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Seon_Doppler_Adaptive_Digital_Semantic_Communication_for_Low_Earth_Orbit_Satellite_Systems.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3390865, "sha256": "50850aef9a3349e29a982251ce801fdf6a9943e0e97c5998d8c70e896cf106d6", "ieee_document_id": "11180041", "ieee_document_url": "https://ieeexplore.ieee.org/document/11180041", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11180041", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Enhancing the Reliability of Multiuser Image Semantic Communication in Wireless Networks", "authors": ["Zhen Huang", "Yunjian Jia", "Jiping Yan", "Wanli Wen", "Liang Liang", "Xinyue Liu", "Xuanguang Wu"], "year": 2025, "publication_date": "2025-08-04", "doi": "10.1109/jiot.2025.3595546", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "20", "pages": "43282-43295", "abstract": "The rapid growth of the mobile Internet has led to an increasing demand for reliable transmission of various data types, particularly images shared among multiple users over wireless networks. Traditional communication systems face challenges in managing large-scale image transmissions. Semantic communication, focusing on conveying meaning rather than raw bits, offers a promising solution. For semantic communication, reliability hinges on two key factors: successful transmission and successful understanding. Taking image semantic communication (ISC) systems as an example, successful transmission ensures the physical delivery of the image, while successful understanding refers to the correct interpretation of its semantics. To address these requirements, we design a semantic extraction and reconstruction module, called STC-based on Swin Transformer and convolutional neural network that enables parallel semantic processing and incorporates enhanced channel-aware attention mechanism, which is then integrated with residual blocks to form a joint source-channel coding (JSCC) model for semantic extraction, compression, and reconstruction. To optimize ISC reliability, we design a system utility function integrating the impacts of successful transmission and comprehension. We formulate a utility maximization problem for joint semantic compression rate (SCR) selection and resource allocation, solved by a carefully-designed joint SCR selection and resource allocation (JSSRA) algorithm based on the hierarchical soft actor-critic method. Simulation results demonstrate that our JSCC model significantly improves image quality compared to other learning-based methods while maintaining computational efficiency. Meanwhile, the JSSRA algorithm enhances system utility by 15%-50% compared to existing resource allocation methods. These results validate the effectiveness and superiority of our proposed methods in multi-user ISC systems.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3595546", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4412939660", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["joint source-channel coding", "JSCC", "semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Huang_Enhancing_the_Reliability_of_Multiuser_Image_Semantic_Communication_in_Wireless_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2182309, "sha256": "ebbc9b3c98421a9b2350cc7f673d33dae553396f201d7fc24d1aff206773f6f3", "ieee_document_id": "11112686", "ieee_document_url": "https://ieeexplore.ieee.org/document/11112686", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11112686", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Explainable Artificial Intelligence Enhance Image Semantic Communication System in 6G-IoT", "authors": ["Mingkai Chen", "Yuangui Gu", "Xiaoming He", "Shu Wang", "Feng Huang", "Lei Wang"], "year": 2025, "publication_date": "2025-09-05", "doi": "10.1109/jiot.2025.3606530", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "5", "pages": "8036-8049", "abstract": "The emerging 6G-IoT paradigm is driving communication toward intelligent services, semantic communication enables efficient semantic sharing via artificial intelligence (AI), significantly boosting communication efficiency. However, current semantic systems suffer from black-box decision-making, while existing explainable artificial intelligence (XAI) methods face two key challenges: explicability granularity mismatch and closed-loop optimization gap. To address these, we propose a semantic communication framework integrated with XAI (XAI-SCS). Specifically, we first design an explainable semantic codec architecture enhanced by Kolmogorov–Arnold Networks (KAN), where traditional fixed activation functions are replaced with learnable and parameterized ones, enabling function-level visualization to improve model explainability. Second, we develop an explainable semantic transmission module driven by contrastive learning that enhances the robustness of semantic transmission, and incorporating a semantic separability metric to quantify channel impacts on semantic integrity. Third, we introduced a KAN-enhanced causal semantic decoder, which integrates counterfactual interventions to generate pixel-level difference maps. We also propose a contrastive explanation consistency metric to evaluate the sensitivity of key features, enhancing the quality of the reconstruction. The experimental results show that our approaches enhance explainability across the entire decision process, achieve a significant accuracy improvement of up to 55% on the CIFAR-10 dataset with a bandwidth compression ratio of 1/25, and also obtain competitive image reconstruction quality in increasing compression levels. The source code is publicly available at: https://github.com/guyuangui/XAI-SCS.git.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3606530", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414008094", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Chen_Explainable_Artificial_Intelligence_Enhance_Image_Semantic_Communication_System_in_6G_IoT.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6368713, "sha256": "a02e5f918d305d576788c6c634893bd54310b26bac34d8dfa5bc6039b41860f2", "ieee_document_id": "11152499", "ieee_document_url": "https://ieeexplore.ieee.org/document/11152499", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11152499", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Federated-Learning-Enabled Cross-Modal Semantic Communication for 6G", "authors": ["Ruochen Huang", "Chen Qiu", "Mingkai Chen", "Changwei Zhang", "Hongbo Zhu"], "year": 2025, "publication_date": "2025-07-25", "doi": "10.1109/jiot.2025.3590596", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "20", "pages": "41608-41624", "abstract": "In view of super-large scale access and dynamic connectivity requirements in 6G, the number of users and data is increasing exponentially, which makes it difficult to achieve sustainable development of communications. Meanwhile, with the development of cross-modal processing, semantic information is highly considered accurate and intelligent, which is expected to bring new ideas for 6G. Therefore, in this study, we propose a novel framework of cross-modal semantic communications to improve the efficiency of the multimodal processing, especially the tactile modal. Meanwhile, we have redesigned the significant aspects of artificial intelligence (AI), such as encoding, transmission, and processing. As federated learning (FL) inherently supports multiple privacy-preserving and security measures, we also introduce FL to assist AI training in cross-modal semantic communications, including the expansion of multimodal data, cross-modal semantic extraction, comprehensive decision-making, and privacy protection. First, in encoding aspects, we propose a hybrid coding for haptic signal coding by deep learning (DL) according to the semantic association between material identification and tactile code optimization. Second, in transmission aspects, we propose a modal-aware resource allocation for the fairness optimization between transmission requirements and network resources with deep reinforcement learning (DRL). Third, in signal processing, low-rank signal reconstruction and immersive quality of experience (QoE) evaluation by DL and machine learning (ML) are provided to improve and quantify users’ experience. In addition, the computational experiments of those key technologies are shown individually and the specification of their probability is discussed separately. Finally, future research topics related to the above issues are suggested.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3590596", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4412718891", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Huang_Federated_Learning_Enabled_Cross_Modal_Semantic_Communication_for_6G.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6786649, "sha256": "b2f7da63e620df2a350cd07015f5cc4b83cb1fb36d327990c7a0181870f3ad4a", "ieee_document_id": "11096603", "ieee_document_url": "https://ieeexplore.ieee.org/document/11096603", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11096603", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "Importance-Aware Robust Semantic Transmission for LEO Satellite–Ground Communication", "authors": ["Cao Hui", "Rui Meng", "Xiaodong Xu", "Shujun Han", "Ping Zhang"], "year": 2025, "publication_date": "2025-12-18", "doi": "10.1109/jiot.2025.3645361", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "5", "pages": "9665-9681", "abstract": "Satellite-ground semantic communication is anticipated to serve a critical role in the forthcoming sixth-generation (6G) mobile networks. Nonetheless, task-oriented data transmission in such systems remains a formidable challenge, primarily due to the dynamic nature of Signal-to-Noise Ratio (SNR) fluctuations and the stringent bandwidth limitations inherent to Low Earth Orbit (LEO) satellite channels. In response to these constraints, we propose an Importance-Aware Robust Semantic Transmission (IRST) framework, specifically designed for scenarios characterized by bandwidth scarcity and channel variability. The IRST scheme begins by applying a segmentation model enhancement algorithm to improve the granularity and accuracy of semantic segmentation. Subsequently, a task-driven semantic selection method is employed to prioritize the transmission of semantically vital content based on real-time Channel State Information (CSI). Furthermore, the framework incorporates a stack-based, SNR-aware channel codec capable of executing adaptive channel coding in alignment with SNR variations. Comparative evaluations across diverse operating conditions demonstrate the superior performance and resilience of the IRST model relative to existing benchmarks. The code is available at https://github.com/lightwindy-ch/IRST.git.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3645361", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4417469988", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Hui_Importance_Aware_Robust_Semantic_Transmission_for_LEO_SatelliteGround_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4864254, "sha256": "015bbb3e6502dde4a63c9678225dc14803a168cbdd4bf03506a8793db5ebb56c", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "In-Band Full-Duplex System for Semantic Communication", "authors": ["Mengran Shi", "Haotai Liang", "Chen Dong", "Xiaodong Xu", "Rui Meng", "Ping Zhang"], "year": 2025, "publication_date": "2025-01-01", "doi": "10.1109/jiot.2025.3598151", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "Driven by the severe self-interference (SI) in in-band full-duplex (IBFD) technologies, which creates extremely harsh communication environments, addressing this challenge has become a critical research focus. Semantic communication technologies, meanwhile, exhibit significant advantages in constrained environments by enabling efficient information transmission with reduced data volume and optimized bandwidth utilization. This article proposes an in-band full-duplex semantic communication (IBFD-SC), which combines IBFD with semantic communication to save transmission volume and improve spectral efficiency. The system incorporates a semantic importance mechanism, which is merged with radio frequency (RF) communication links. A semantic importance mapping module is introduced to map semantic symbols to baseband signals, considering both channel conditions and the significance of semantic symbols. Additionally, a nonlinear interference cancellation method is designed to eliminate SI, ensuring the integrity and reliability of key semantic information during communication. Experimental results demonstrate that the integration of semantic importance effectively mitigates interference and improves communication performance, particularly under low signal to interference plus noise ratio (SINR) conditions.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3598151", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4413157553", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Shi_In_Band_Full_Duplex_System_for_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 9870373, "sha256": "ee04e422e7f19fea0ea81ad4af17b4ab844278d64c6e78188a10f3662e6231bc", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "IoT-Enabled Traffic Management System Using Vehicle Count Prediction in a Semantic Communication Framework", "authors": ["Sachin Kadam", "Dong In Kim"], "year": 2025, "publication_date": "2025-06-20", "doi": "10.1109/jiot.2025.3581778", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "36258-36273", "abstract": "An effective traffic management system is crucial to smart city growth. Consequently, the significance of IoT devices is increasing. Numerous IoT devices, including cameras, are commonly positioned along major roads in a smart city. These IoT devices, embedded with computing and transmitting capabilities, collect data from cameras and then relay it to the central traffic controller (CTC) responsible for managing traffic flow. In our study, we introduce a novel framework termed semantic communication (SemCom), which integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. The SemCom model employs a semantic encoder within each IoT device to extract pertinent information from raw images. This encoded data is transmitted to the CTC as symbols by the transmitter of the IoT device. Subsequently, the CTC’s semantic decoder utilizes this sequence of symbols to predict vehicle counts on respective roads and devise traffic management strategies accordingly. To enhance the quality of experience (QoE), we formulate an optimization problem considering vehicle user safety, IoT device transmission power, prediction accuracy, and semantic entropy. Through numerical analysis, we demonstrate that the SemCom model significantly reduces overhead by 54.42% compared to conventional source encoder/decoder models. Moreover, simulation results showcase the superiority of our proposed model in terms of mean absolute error (MAE) and QoE metrics over existing state-of-the-art approaches. Since vehicle count prediction is pivotal in traffic management, our SemCom framework offers a promising avenue for efficient and accurate vehicle count prediction, contributing to more effective traffic management in smart cities.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3581778", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4411471994", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Kadam_IoT_Enabled_Traffic_Management_System_Using_Vehicle_Count_Prediction_in_a_Semantic_Communication_Framework.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2044517, "sha256": "7d4c926f94618863c807d5411edeab6f4482e3ae529abf0b90a22da894d3bf4b", "ieee_document_id": "11045677", "ieee_document_url": "https://ieeexplore.ieee.org/document/11045677", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11045677", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "Joint Source-Channel Noise Adding With Adaptive Denoising for Diffusion-Based Semantic Communications", "authors": ["Chengyang Liang", "Dong Li"], "year": 2025, "publication_date": "2025-01-01", "doi": "10.1109/jiot.2025.3589398", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "Semantic communication (SemCom) aims to convey the intended meaning of messages rather than merely transmitting bits, thereby offering greater efficiency and robustness, particularly in resource-constrained or noisy environments. In this paper, we propose a novel framework which is referred to as joint source-channel noise adding with adaptive denoising (JSCNA-AD) for SemCom based on a diffusion model (DM). Unlike conventional encoder-decoder designs, our approach intentionally incorporates the channel noise during transmission, effectively transforming the harmful channel noise into a constructive component of the diffusion-based semantic reconstruction process. Besides, we introduce an attention-based adaptive denoising mechanism, in which transmitted images are divided into multiple regions, and the number of denoising steps is dynamically allocated based on the semantic importance of each region. This design effectively balances the reception quality and the inference latency by prioritizing the critical semantic information. Extensive experiments demonstrate that our method significantly outperforms existing SemCom schemes under various noise conditions, underscoring the potential of diffusion-based models in next-generation communication systems.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3589398", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4412444882", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2025_Liang_Joint_Source_Channel_Noise_Adding_With_Adaptive_Denoising_for_Diffusion_Based_Semantic_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 480956, "sha256": "ba65ac46647002aae41ba1f2b8dd305cc9fdee6b79ef82602a362bc131c0b9c7", "download_source": "https://arxiv.org/pdf/2505.09644v2", "arxiv_id": "2505.09644", "pdf_pages": 4, "pdf_title_token_match": 1.0}
{"title": "Knowledge Graph-Enhanced Triplet Semantic Communication System for Low-Altitude IoT Transmission", "authors": ["Zhiyue Zhang", "Shaojie Wang", "Quan Zhou", "Licui Ma", "Ruijie Wen", "Junsheng Mu"], "year": 2025, "publication_date": "2025-08-05", "doi": "10.1109/jiot.2025.3596170", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "4", "pages": "5669-5681", "abstract": "Semantic communication (SemCom) is a crucial direction for future wireless communication, aiming to achieve efficient and low-redundancy communication by extracting and transmitting semantic information. It has significant application value in emerging scenarios with limited bandwidth and intensive data, such as the Low-Altitude Internet of Things (LA-IoT). However, existing SemCom frameworks face limitations in semantic compression and generalization. To address these challenges, this paper proposes a knowledge graph-enhanced SemCom system. The proposed system leverages a shared knowledge graph (KG) and deep learning models to extract semantic information from the input text, represented in the form of entity–relation–entity triples. By aligning these triples with existing knowledge in the KG, the system distinguishes between common semantics and unique semantics. Common semantics are reconstructed at the receiver using the KG, whereas unique semantics are compressed and transmitted through the channel and then fused with the shared knowledge to reconstruct the original text. Furthermore, considering the structured nature of triple-based information, we propose a structured attention mechanism to enhance transmission accuracy. Experimental results demonstrate that the proposed system achieves a BLEU score exceeding 0.9 and a METEOR score exceeding 0.93 under high SNR conditions, outperforming baseline methods in semantic fidelity while significantly reducing redundancy.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3596170", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4412985000", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Zhang_Knowledge_Graph_Enhanced_Triplet_Semantic_Communication_System_for_Low_Altitude_IoT_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2228215, "sha256": "5d6de3d4efbcb3b3431604bf8a15ab86ed32792f49211124159aa641eeea87c1", "ieee_document_id": "11114427", "ieee_document_url": "https://ieeexplore.ieee.org/document/11114427", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11114427", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Low-Bitrate High-Quality Digital Semantic Communication Based on RVQGAN", "authors": ["Xiaojiao Chen", "Jing Wang", "Jingxuan Huang", "Ming Zeng", "Zhong Zheng", "Zesong Fei"], "year": 2025, "publication_date": "2025-02-13", "doi": "10.1109/jiot.2025.3534462", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "10", "pages": "13525-13537", "abstract": "Digital semantic communication has attracted considerable attention attributed to its potential for integration with modern digital communication systems, which has demonstrated significant performance gains. However, despite its ability to save transmission bandwidth, digital semantic communication can degrade the performance of tasks at the receiver, particularly in low-bitrate scenarios. In this article, we propose a novel low-bitrate digital semantic communication method based on a generative model for speech transmission to achieve high-quality reconstructed speech at low-bitrate transmission. In particular, we first investigate a multiscale semantic codec based on residual vector quantization with a generative adversary network (RVQGAN) model for extracting semantic information and obtaining high speech reconstruction quality while transmitting at a low bitrate. We then, design a channel noise suppression (CNS) module based on U-Net to alleviate the channel effect at low signal-to-noise ratio (SNR) by restoring high-quality semantic features, which is capable of improving the performance of the proposed method under challenging channel conditions. Moreover, a Transformer-based code predictor is utilized to further improve the robustness of the proposed method by accounting for both the channel impact and reconstruction quality. Finally, a three-stage training strategy is also presented in this article to ensure the effective operation of the proposed multiscale semantic codec, CNS module, and code predictor module. Experimental results demonstrate that the proposed method operating at 3 kb/s can save at least 50% of bandwidth while achieving higher speech restoration quality than the baseline method.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3534462", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4407450091", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Chen_Low_Bitrate_High_Quality_Digital_Semantic_Communication_Based_on_RVQGAN.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2798755, "sha256": "1a71e8426e3d741efe9ed5c3565a3fddb4973cbe170928566000e7c51a923b50", "ieee_document_id": "10886956", "ieee_document_url": "https://ieeexplore.ieee.org/document/10886956", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10886956", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Mobility-Aware Semantic Offloading With NOMA in Edge-Enabled IoT Networks", "authors": ["Yaohua Li", "Linyu Huang", "Qian Ning", "Chengping Zhao"], "year": 2025, "publication_date": "2025-09-05", "doi": "10.1109/jiot.2025.3606562", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "22", "pages": "48735-48750", "abstract": "The rapid development of Internet of Things (IoT) applications has imposed stringent demands on low-latency and energy-efficient task offloading. Although mobile edge computing (MEC) provides nearby computing capabilities, conventional data-driven offloading approaches suffer from redundant transmission and limited scalability under constrained wireless and computational resources. To address this, we propose a general semantic-aware offloading framework that integrates semantic communication, Non-Orthogonal Multiple Access (NOMA), MEC, and user mobility prediction in edge-enabled IoT networks. The offloading user first performs local semantic extraction based on a tunable semantic factor that influences both the local computation cost and the transmitted data volume, and theoretical modeling focuses on adjustable granularity to derive an approximate optimal theoretical bound. The resulting semantic information is then transmitted to the MEC server for task execution. We formulate a joint optimization problem to minimize total user-side energy consumption by optimizing offloading decisions, semantic factors, transmit power, and time-slot scheduling. To solve this, we develop a Mixed-Integer Linear Programming (MILP)-based solution via linearization and piecewise approximation of the original non-convex problem, and we further propose a low-complexity heuristic algorithm for practical feasibility.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3606562", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414008009", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic Offloading"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Li_Mobility_Aware_Semantic_Offloading_With_NOMA_in_Edge_Enabled_IoT_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2369711, "sha256": "73b50c3e4cf96cc4473c2b3e652d3fbd0d2e27164e04929afa4df39bc3070d35", "ieee_document_id": "11152545", "ieee_document_url": "https://ieeexplore.ieee.org/document/11152545", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11152545", "pdf_pages": 16, "pdf_title_token_match": 0.8333}
{"title": "MRCoach: A Real-Time IoT-Enabled Mixed Reality System With Semantic-Aware Transmission for Smart Sports and Personalized Coaching", "authors": ["Mingrui Yin", "Sohom Sen", "Yongjie Guan", "Dhananjay Jagdish Dubey", "Xinran Chen", "Xueyu Hou", "Tao Han", "Nirwan Ansari"], "year": 2025, "publication_date": "2025-09-22", "doi": "10.1109/jiot.2025.3612408", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "24", "pages": "52466-52473", "abstract": "Real-time transmission of large-scale data, high computational demands, and resource limitations on edge devices pose significant challenges for intelligent sports systems. The proliferation of Internet of Things (IoT) technologies has catalyzed the rise of Smart Sport, where wearable sensors, cameras, and intelligent algorithms are integrated to revolutionize athletic training. Despite this transformation, access to professional coaching remains constrained by factors such as time, cost, and scalability. A critical limitation of existing remote coaching approaches is their inability to perform effective spatio-temporal analysis, hindering comprehensive evaluation and refinement of athletic performance. This paper introduces MRCoach, a mixed reality-based, immersive, and interactive sports coaching system that enables data-driven training without requiring in-person supervision. MRCoach reconstructs 3D volumetric avatars of both learners and expert athletes, allowing users to visualize and compare their movements side-by-side in a mixed reality environment for intuitive skill refinement. To ensure responsive and efficient feedback, we propose an adaptive semantic transmission strategy that prioritizes sport-relevant joints, thereby reducing latency and bandwidth requirements without sacrificing accuracy. Furthermore, a 3D sports analysis framework is developed to evaluate motion based on normalized joint positions, velocities, and accelerations. This framework computes real-time similarity scores and delivers actionable guidance to learners. Experimental results across four sports—tennis, soccer, basketball, and baseball—demonstrate MRCoach’s effectiveness in providing personalized, real-time training experiences. Compared to state-of-the-art baselines such as MagicStream, ExPose, and PIXIE, MRCoach achieves significantly lower end-to-end latency (79.9 ms) and higher frame rates (≥ 56 FPS), while maintaining accurate pose tracking and high avatar fidelity.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3612408", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "VQ semantic communication", "semantic communication codebook", "digital JSCC semantic communication", "semantic-aware channel coding"], "openalex_id": "https://openalex.org/W4414404576", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic transmission", "Semantic-Aware Transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Yin_MRCoach_A_Real_Time_IoT_Enabled_Mixed_Reality_System_With_Semantic_Aware_Transmission_for_Smart_Sports_and_Perso.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 752319, "sha256": "dbc138437392337510416e451448adce471e9ee2b7772ca20298683443723714", "ieee_document_id": "11175204", "ieee_document_url": "https://ieeexplore.ieee.org/document/11175204", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11175204", "pdf_pages": 8, "pdf_title_token_match": 1.0}
{"title": "Multitask Semantic Communication: A Mutual Information-Aided Semi-Supervised Approach", "authors": ["Yining Wang", "Wenqiang Yi", "Shujun Han", "Xiaodong Xu", "Ping Zhang", "Arumugam Nallanathan"], "year": 2025, "publication_date": "2025-09-09", "doi": "10.1109/jiot.2025.3607792", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "22", "pages": "48388-48404", "abstract": "In this article, we design an end-to-end digital semantic communication system to transmit semantic symbols that simultaneously facilitate image classification tasks and reconstruction tasks. By training a mutual information-assisted joint source-channel coding (MIJSCC) framework, the learned semantic representation can incorporate both pixel-level generative information for reconstruction and structural discriminative information for classification, which are obtained label-free via global and local mutual information estimation and maximization, as well as mean-square error (MSE) minimization. Then, the high-resolution semantic representation is quantized into finite constellation symbols to satisfy the hardware constraint on discrete control in practical radio frequency systems. Considering dynamic channel conditions in practical communication systems, we further design an adaptive MIJSCC (A-MIJSCC) framework with attention-based semantic enhancement (A-MIJSCC), which allows for the sequential activation of varying dimensions of the semantic representation according to channel signal-to-noise ratio. Compared to existing semantic communication frameworks that are dominated by end target and labels, the MIJSCC addresses the semi-supervised learning of intermediate semantics. Simulation results show that the proposed MIJSCC supports both image classification and reconstruction via task-agnostic semantic extraction, whose performance surpasses the benchmark frameworks. It is also demonstrated that the A-MIJSCC method facilitates the adaptive semantic transmission under varying channel conditions, which effectively reduces the transmission overhead while preserving task performance.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3607792", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414165919", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["joint source-channel coding", "semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Wang_Multitask_Semantic_Communication_A_Mutual_Information_Aided_Semi_Supervised_Approach.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2629920, "sha256": "2d2e0cef41b19ee3a42436e2e016b3901f67231fd7042bd833efb51af70d3c0a", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "Multiuser Content-Style Adaptive Semantic Communication for Image Transmission", "authors": ["Mengshu Song", "Nan Ma", "Haotai Liang", "Chen Dong", "Weizhi Li", "Jianqiao Chen", "Yijing Lin", "Ping Zhang"], "year": 2025, "publication_date": "2025-06-19", "doi": "10.1109/jiot.2025.3581393", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "36063-36078", "abstract": "With the rapid development of Internet of Things (IoT) technology, an increasing number of resource-constrained devices operate in dynamic and heterogeneous network environments, posing challenges for efficient image transmission. Multi-user semantic communication (SC) enables reduced bandwidth consumption and enhanced noise resilience by understanding the intrinsic meaning of information and sharing common semantic features across devices, offering great potential for widespread applications in various IoT scenarios. However, current multi-users SC approaches for image transmission lack adaptability and fail to consider both content and style features, leading to degraded image reconstruction quality. Moreover, semantic redundancy among devices remains underutilized, limiting bandwidth efficiency in IoT networks. To address these limitations, in this paper, a novel multi-user content-style adaptive semantic communication system for image transmission in IoT scenarios is proposed. Specifically, a dual-branch semantic information extraction and adaptive recovery scheme is first established, which simultaneously captures and adaptively fuses semantic content and style features to improve reconstruction quality. Secondly, an adaptive common information extraction and enhanced coding module is introduced for resource-limited IoT devices, which dynamically adjusts the transmission rate based on varying channel conditions and the computational capabilities of different users, further optimizing communication performance. Finally, experimental results show that the proposed method improves peak signal-to-noise (PSNR) by at least 10% under poor SNR conditions for multi-users semantic communication, compared to baseline methods.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3581393", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4411446205", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Song_Multiuser_Content_Style_Adaptive_Semantic_Communication_for_Image_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4223929, "sha256": "f53a4bdd81be2e2f5c9e48f3f92c963d12bf4127efc5b13ea56e6e91727a7c13", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "Multiuser Semantic Communication With Federated Learning for Intelligent Search Service", "authors": ["Meiyu Sun", "Dapeng Wu", "Puning Zhang", "Ruyan Wang"], "year": 2025, "publication_date": "2025-03-28", "doi": "10.1109/jiot.2025.3555437", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "13", "pages": "24313-24328", "abstract": "Intelligent search enables users to access information from the Internet quickly, but existing schemes fail to achieve accurate semantic awareness and reliable information transmission, especially in constrained communication conditions, which degrade search accuracy and personalized user experience. To address these challenges, we propose a multiuser semantic communication system to perform personalized search (PS) tasks, named MU-SemCom-PS. In particular, the system introduces a novel semantic encoder at the transmitter to deeply extract user-specific search semantics by analyzing search history from multiple perspectives, and designs a semantic decoder at the receiver to recover and enhance search semantics by leveraging implicit correlations among users, thus the PS tasks are performed based on the recovered search semantics. To optimize the PS tasks for all users, the federated learning (FL) framework is leveraged to jointly train the MU-SemCom-PS system through knowledge collaboration and sharing. Experimental results show that the proposed scheme significantly improves search accuracy and robustness under constrained communication conditions.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3555437", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4408941359", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Sun_Multiuser_Semantic_Communication_With_Federated_Learning_for_Intelligent_Search_Service.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5885262, "sha256": "3019007bbfa544ba928baf5a97a4b52d49a433e8eccc4b693d38ca9572881082", "ieee_document_id": "10943126", "ieee_document_url": "https://ieeexplore.ieee.org/document/10943126", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10943126", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "NORRIS: Noise-Resilient and Resource-Efficient Semantic Encoded Point Cloud Data Transmission for Internet of Things Communications", "authors": ["Saleh Safarnejad", "Mohammad Hosein Soheilian", "Bardia Safaei"], "year": 2025, "publication_date": "2025-04-09", "doi": "10.1109/jiot.2025.3559435", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "13", "pages": "25720-25731", "abstract": "Employing point cloud and 3D data has recently proliferated with the advent of emerging IoT applications, e.g., Autonomous Vehicles (AV), and Augmented Reality/Virtual Reality (AR/VR), with their resource-limited IoT devices equipped with Light Detection and Ranging (LiDAR) sensors. While offloading the point cloud computations could improve energy-efficiency, the imposed bandwidth and energy dissipation due to transmitting a significant volume of 3D data is still challenging. This issue gets more complicated due to the instability caused by the noisy channel conditions, which leads to the cliff effect and reduced object detection accuracy. Accordingly, this paper introduces NORRIS; a semantic-based point cloud data transmission technique that mitigates transmitted data while enhancing resilience against channel noise. NORRIS utilizes joint source-channel coding in combination with transfer learning. It emphasizes efficiency and performance, providing a lightweight model that sustains excellent performance across different channel conditions, making it ideal for resource-constraint IoT devices. While NORRIS provides high detection accuracy, it significantly reduces the inference time and model complexity. The effectiveness of NORRIS is evaluated and compared with state-of-the-art through a comprehensive set of experiments. Results demonstrate that NORRIS successfully overcomes the cliff effect while guaranteeing a higher performance in lower Signal-to-Noise Ratio (SNR) conditions. It also reduces the transmitted data volume by up to 10x, while accelerating meaning extraction by 5x. It also enhances accuracy by approximately 10% in various SNR scenarios. Furthermore, NORRIS maintains stable classification performance under noise, with only a 5% accuracy drop, in contrast to the 60% decrease observed in existing approaches.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3559435", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4409311054", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["joint source-channel coding"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Safarnejad_NORRIS_Noise_Resilient_and_Resource_Efficient_Semantic_Encoded_Point_Cloud_Data_Transmission_for_Internet_of_Thi.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3440690, "sha256": "a0ce89b3c3ea4e7a5c4be62396fa1dcd0f8c4bcda112d2a75201f0111917a526", "ieee_document_id": "10960341", "ieee_document_url": "https://ieeexplore.ieee.org/document/10960341", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10960341", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "On Harnessing Semantic Communication With Natural Language Processing", "authors": ["Shiva Raj Pokhrel", "Te’ Claire"], "year": 2025, "publication_date": "2025-01-13", "doi": "10.1109/jiot.2025.3528937", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "11", "pages": "15022-15031", "abstract": "Through experimental endeavors, we explore the intersection of semantic communication (SemCom) and natural language processing (NLP) to address gaps in SemCom models, focusing on reducing ambiguity and enhancing 6G communication. Our approach involves two phases: 1) Phase 1: Designing an NLP-based system for fair classification, leveraging techniques, such as unsupervised style transfer and zero-shot learning to align human intuition with semantic specifications. 2) Phase 2: Developing modules to minimize and evaluate SemCom performance under channel impairments, integrating language models like DistilBERT and RoBERTa. Results are evaluated using area under the curve receiver operating characteristic (AUC-ROC) metrics across diverse classifiers. Implementation details are publicly available on GitHub. We provide invaluable insights toward learning to harness SemCom with NLP.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3528937", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4406321300", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Pokhrel_On_Harnessing_Semantic_Communication_With_Natural_Language_Processing.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5596603, "sha256": "01941cd6f4d87b98a11c87f1683ed361fa32f0c7154c9334ef273ff573fe6714", "ieee_document_id": "10839323", "ieee_document_url": "https://ieeexplore.ieee.org/document/10839323", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10839323", "pdf_pages": 10, "pdf_title_token_match": 1.0}
{"title": "Online Energy Efficient Multimodal Probabilistic Semantic Communication", "authors": ["Jianxin Dai", "Jingjing Li", "Zhouxiang Zhao", "Zhaohui Yang", "Jianglin Ye", "Qianqian Yang", "Chongwen Huang", "Zhaoyang Zhang"], "year": 2025, "publication_date": "2025-05-21", "doi": "10.1109/jiot.2025.3572209", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "1", "pages": "69-86", "abstract": "In this paper, we investigate an uplink multi-modal probabilistic semantic communication (PSCom) system based on probability graph in the satellite scenario. The system consists of both: semantic computation and traditional communication. Firstly, at the user end, the transmitted data is compressed based on the probabilistic graph. Then, the compressed data is transmitted to the satellite, which uses the same probabilistic graph to recover the received data. In the considered model, this paper addresses an optimization problem for multi-modal multi-user semantic communication across multiple time slots. An optimization problems formulated aiming to minimize the total energy consumption of the PSCom system, with satisfying the transmission time, transmission power, transmission bandwidth, local computation frequency, and transmission data requirements. To solve this problem, the Lyapunov drift-plus-penalty function based on online optimization is first used to transform the multi-slot problem into a stochastic single-slot problem, thereby converting the optimization problem into a trade-off between system energy consumption and queue length. Subsequently, an alternating algorithm is proposed to iteratively optimize, semantic compression rate, local computation frequency, transmission bandwidth, transmission power, and time allocation variables. Finally, simulation experiments demonstrates the effectiveness of the proposed algorithm.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3572209", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4410582763", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Dai_Online_Energy_Efficient_Multimodal_Probabilistic_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4185912, "sha256": "7bfc93f3067301ea4fea1b7fa99585c8fd594a14b68c87f06fcde8af34c291c6", "ieee_document_id": "11008535", "ieee_document_url": "https://ieeexplore.ieee.org/document/11008535", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11008535", "pdf_pages": 18, "pdf_title_token_match": 0.8571}
{"title": "Performance Optimization for Multicell Multihop Semantic 6G Cloudified Networks Considering Compression, Transmission, and Computation", "authors": ["Haiwen Niu", "Luhan Wang", "Zhaoming Lu", "Xiangming Wen", "Paulo Marques"], "year": 2025, "publication_date": "2025-07-25", "doi": "10.1109/jiot.2025.3592241", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "19", "pages": "41197-41214", "abstract": "Deep learning (DL)-enabled semantic communication has been widely regarded as a promising 6G technology due to its efficient wireless transmission capabilities, providing a novel technological foundation for bandwidth-constrained Internet of Things (IoT) scenarios. Combining semantic communication with classical multicell network architectures, which are commonly employed in long-distance IoT communications, is essential for enabling its large-scale deployment in future 6G systems. However, the performance optimization of semantic transmission in this scene still faces significant challenges due to the complex coupling among compression, transmission, and computation under resource-limited conditions, which is difficult to solve by traditional methods due to the closed box nature of the artificial intelligence models used. To address this problem, in this article, we formulate an optimization problem to minimize the weighted sum of end-to-end latency and information reconstruction performance by adjusting the compression control strategy, radio block assignment, and computing resource allocation decision. The case of image transmission is investigated. To address this nonconvex mixed-integer nonlinear programming problem, we adopt a lower bound approximation method to decompose the formulated problem and design an optimization method combining the Lagrange multiplier method and multiagent deep reinforcement learning algorithm. We also introduce an image reconstruction performance prediction model to empower the agent with semantic-aware optimization ability. The results demonstrate that the proposed method can achieve superior performance compared to other approaches.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3592241", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4412719136", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["semantic communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Niu_Performance_Optimization_for_Multicell_Multihop_Semantic_6G_Cloudified_Networks_Considering_Compression_Transmis.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3540735, "sha256": "663be0ff4cd00f8814a89a0443c185c9dc84bc9236c562192784863e55cc9562", "ieee_document_id": "11096074", "ieee_document_url": "https://ieeexplore.ieee.org/document/11096074", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11096074", "pdf_pages": 18, "pdf_title_token_match": 0.9091}
{"title": "RIS-Assisted Semantic Communication for Real-Time 3-D Reconstruction via Gaussian Splatting", "authors": ["Wang Liu", "Yuanmeng Zhang", "Qingtao Zeng", "Likun Lu", "Anping Xu", "Wenjing Li"], "year": 2025, "publication_date": "2025-10-01", "doi": "10.1109/jiot.2025.3616351", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "24", "pages": "53190-53201", "abstract": "Recently, with the advancement of technologies such as virtual reality and the metaverse, remote 3D reconstruction has gained increasing attention. However, achieving fast and high-quality 3D reconstruction in complex communication environments remains a major challenge. To address this, we propose R-SVRSC, a semantic communication system for single-view 3D reconstruction based on 3D Gaussian Splatting (3D GS). Specifically, the transmitter extracts and transmits semantic information from a single image, instead of transmitting complex 3D data. At the receiver, a Semantic-to-Gaussian Mapper (SGM) directly maps the received deep semantic features into 3D Gaussians, which are then rapidly rendered using a Gaussian rasterizer. Furthermore, to combat the performance degradation caused by multipath fading and channel instability, we introduce a RIS-assisted enhancement to the proposed semantic communication system. A deep learning (DL)-based RIS phase shift prediction module is integrated into the transmitter and jointly trained with the entire system in an end-to-end manner, enabling intelligent reconstruction of the wireless channel. Experimental results show that the proposed system achieves efficient rendering and consistently high-quality 3D reconstruction under low signal-to-noise ratio (SNR) conditions across various real-world channel scenarios.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3616351", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414693481", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Liu_RIS_Assisted_Semantic_Communication_for_Real_Time_3_D_Reconstruction_via_Gaussian_Splatting.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2309798, "sha256": "d560bc8c6d7416dd37322753e364123232017676c0fd9f2eac2c769c7c0d6094", "ieee_document_id": "11187309", "ieee_document_url": "https://ieeexplore.ieee.org/document/11187309", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11187309", "pdf_pages": 12, "pdf_title_token_match": 1.0}
{"title": "SCSC: The Super Compressed Semantic Communication Transmission Method for Images and Video Frames", "authors": ["Jia Guo", "Bowen Sun", "Jinqi Zhu", "Weijia Feng", "Shuqing He", "Wanli Xue"], "year": 2025, "publication_date": "2025-08-12", "doi": "10.1109/jiot.2025.3598074", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "20", "pages": "43731-43741", "abstract": "Semantic communication is a transformative approach for efficient data transmission in bandwidth-constrained IoT networks, particularly for device-to-device (D2D) communication and real-time systems. To address the challenges of high-quality data reconstruction in low-bandwidth scenarios, such as IoT-enabled underwater or wireless networks, this article proposes a novel framework for ultraefficient image and video frame compression. The framework employs an optimized feature extraction method to capture essential semantic information from images or video frames, converting them into compact grayscale representations to minimize data volume for bandwidth-constrained devices. At the receiver, a dual-decoding strategy reconstructs structural details using lightweight semantic reconstruction techniques, followed by color attribute recovery, ensuring high visual quality in real-time applications. This approach enhances transmission efficiency, reduces latency, and maintains information integrity in resource-limited IoT environments. Experimental results on the Kodak dataset show a 7.02% compression ratio (PSNR = 13 dB), with a 9.76% improvement in bandwidth efficiency compared to existing methods. For 4K images and video frames, the framework achieves a 98.35% MS-SSIM retention rate under SNR conditions of 1–15 dB, demonstrating robust performance for real-time IoT communication.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3598074", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W4413155214", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Guo_SCSC_The_Super_Compressed_Semantic_Communication_Transmission_Method_for_Images_and_Video_Frames.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5029495, "sha256": "09d51b16678082de9df5472f4bc22cc05d0e9007e47f11246f132f96cad148d4", "ieee_document_id": "11123543", "ieee_document_url": "https://ieeexplore.ieee.org/document/11123543", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11123543", "pdf_pages": 11, "pdf_title_token_match": 1.0}
{"title": "sDMCM—A Semantic Digital Modulation Constellation Mapping Scheme for Semantic Communication", "authors": ["Lei Teng", "Wannian An", "Chen Dong", "Xiaodong Xu"], "year": 2025, "publication_date": "2025-02-25", "doi": "10.1109/jiot.2025.3545667", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "12", "pages": "20885-20901", "abstract": "The current state of semantic communication research is primarily based on analog modulation, whereas digital communication is more prevalent in practical applications. However, traditional digital modulation constellation mapping designs are not suitable for semantic communication, as they are primarily focused on minimizing the bit error rate of the transmitted data rather than the change in the numerical value of the semantic information. To address this issue, a semantic digital modulation constellation mapping (sDMCM) scheme based on pulse amplitude modulation (PAM)/quadrature amplitude modulation (QAM) is proposed, that considers the internal correlation of the semantic information. In addition, this article proposes using the mean-squared error (MSE) of the semantic information between the transmitter and receiver as a performance metric for semantic communication. This article also provides the theoretical MSE performance of the proposed sDMCM through formula derivation. Finally, the simulation results match the theoretical results, demonstrating the rationality of the theoretical derivation. Comparing the proposed sDMCM scheme with traditional mapping schemes, such as Gray, Pseudo-Gray (Pe-Gray), and structural quadrant (SQ) constellations in an image semantic communication system under the additive white Gaussian noise (AWGN) channel, the restored image can tolerate an SNR drop of about 3 dB while maintaining the same multiscale structure similarity (MS-SSIM) performance. In addition, sDMCM is applied in an industrial Internet of Things semantic communication system to validate its role in IoT applications.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3545667", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4407937717", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Teng_sDMCMA_Semantic_Digital_Modulation_Constellation_Mapping_Scheme_for_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 11004529, "sha256": "8c87e422b4679997f4d7608b4c8c7c2ff108b71c1436e51069bd5b59fe025fea", "ieee_document_id": "10902381", "ieee_document_url": "https://ieeexplore.ieee.org/document/10902381", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10902381", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels", "authors": ["Shuai Ma", "Zhiye Sun", "Bin Shen", "Youlong Wu", "Hang Li", "Guangming Shi", "Shiyin Li", "Naofal Al‐Dhahir"], "year": 2025, "publication_date": "2025-02-04", "doi": "10.1109/jiot.2025.3538764", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "11", "pages": "17123-17136", "abstract": "In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3538764", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4407155151", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2502.03949", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Broadcast"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2025_Ma_Semantic_Feature_Division_Multiple_Access_for_Digital_Semantic_Broadcast_Channels.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4010942, "sha256": "88d81c2dff79921ca773ab573abb23aa028b243a770e25c195b7ea5fcd711e54", "download_source": "https://arxiv.org/pdf/2502.03949", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Semantic Image Communication Based on Swin Transformer for Satellite IoE", "authors": ["Wenyuan Wang", "Wupeng Xie", "Chaowei Wang", "Jisong Xu", "Yunze Zhang", "Fan Jiang", "Lexi Xu", "Zhi Zhang", "Wenjun Xu"], "year": 2025, "publication_date": "2025-10-28", "doi": "10.1109/jiot.2025.3626402", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "1", "pages": "392-404", "abstract": "This paper addresses the challenges of image transmission in satellite communication networks, where bandwidth constraints, high interference, and latency issues significantly limit conventional transmission methods. We propose a novel semantic communication framework that adapts to various computational capabilities of receiving terminals in Internet of Everything (IoE). Our approach leverages the Swin Transformer V2 architecture to extract and transmit task-relevant semantic features rather than raw image data, significantly reducing bandwidth requirements while maintaining high reconstruction quality. The proposed system dynamically adjusts its encoding and decoding processes based on receiver computational capacities, enabling efficient image transmission to heterogeneous terminals ranging from high-performance stations to resource-constrained devices. Extensive experiments on various datasets demonstrate that our framework outperforms conventional JPEG+LDPC schemes and state-of-the-art deep learning-based approaches in terms of both PSNR performance and semantic communication utility across various signal-to-noise ratios. The framework shows particular robustness in low-SNR and low-CBR environments, addressing the “efficiency-compatibility” dilemma in resource-constrained satellite communications.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3626402", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4415624402", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Wang_Semantic_Image_Communication_Based_on_Swin_Transformer_for_Satellite_IoE.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5029951, "sha256": "85cad6ba839186302570c8876b6096d640277d5e4ada754f2a6722437435bf44", "ieee_document_id": "11219033", "ieee_document_url": "https://ieeexplore.ieee.org/document/11219033", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11219033", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Semantic Importance-Aware Image Transmission in V2X Networks", "authors": ["Anna Cai", "Liang Wang", "Yaguang Lin", "Cong Liu", "Pengcheng Qian"], "year": 2025, "publication_date": "2025-06-23", "doi": "10.1109/jiot.2025.3582442", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "36471-36487", "abstract": "Traditional communication focuses on bit accuracy, while semantic communication improves efficiency by considering the meaning of the data. This paper introduces semantic communication to intelligent transportation systems (ITS), specifically image tasks in vehicle-to-everything (V2X) networks. We propose an adaptive signal-to-noise ratio (SNR) image semantic communication model (ASISC) to address the dynamic nature of V2X environments. An evaluation of the real utility function (ERUF) based on task performance is proposed, which takes into account the semantic transmission rate and the semantic energy consumption. We define the semantic importance scores (SIS) to quantify the complexity of image content. To maximize the ERUF and SIS for image transmission, an optimization problem is formulated to promote high SIS image transmission in V2X networks. In particular, to address the interpretability challenge of neural networks, we propose an independent univariate approach consisting of a content equalization sampling step and an approximate modeling step, to transform the original optimization problem into decoupled power allocation and transmission order subproblems. A ternary search algorithm is used for power allocation, and a distance-based transmission order scheme (DistO) is proposed to give preferential treatment to high SIS image tasks. Simulation results on the CIFAR10 dataset demonstrate that our method outperforms the benchmark schemes, especially in high SIS scenarios, and the transmission efficiency is greatly improved. The proposed scheme is easy to implement and exhibits excellent performance in the V2X networks.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3582442", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4411550845", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Cai_Semantic_Importance_Aware_Image_Transmission_in_V2X_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3171292, "sha256": "10bb1fb21f630b4f8bd4e9c2a7e5683c894f426f79f82486fc3312c659e6611a", "ieee_document_id": "11048572", "ieee_document_url": "https://ieeexplore.ieee.org/document/11048572", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11048572", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "Semantic-Importance-Aware Communication Over MIMO Fading Channels", "authors": ["Haotai Liang", "Chen Dong", "Wannian An", "Zhicheng Bao", "Xiaodong Xu", "Rui Meng"], "year": 2025, "publication_date": "2025-07-04", "doi": "10.1109/jiot.2025.3586038", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "18", "pages": "38540-38555", "abstract": "Semantic communication, a promising paradigm for next-generation wireless systems, optimizes the representation of semantic information and its resilience to channel effects, outperforming traditional systems in low signal-to-noise ratio (SNR) environments. However, most existing frameworks focus on Single-Input Single-Output (SISO) channels which limits their use in multi-antenna systems. To address this gap, we propose Semantic Importance-Aware Communication (SIAC-MIMO), a system designed for Multiple-Input Multiple-Output (MIMO) fading channels. SIAC-MIMO integrates semantic symbol inequality with advanced channel-aware techniques. SIAC-MIMO prioritizes critical semantic symbols, adapts transmission to MIMO channel states, and employs Orthogonal Model Division Multiple Access (O-MDMA) for multi-user broadcasting to mitigate interference while enhancing scalability. A bilateral progressive training algorithm is introduced to align semantic allocation with channel eigenmodes. To evaluate the effectiveness of this system, a theoretical framework is developed to analyze semantic performance metrics, such as semantic information distortion and semantic outage probability. The experiments across 2W2 to 64W64 MIMO setups demonstrate SIAC-MIMO’s superiority, achieving 5–18% improvements in Mean Structural Similarity Index Measure (MS-SSIM) at low SNR in single-user scenarios and 16–23% improvements in multi-user MIMO setups compared to traditional source-channel separation schemes, highlighting the system’s potential for efficient and robust communication.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3586038", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4412030712", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Liang_Semantic_Importance_Aware_Communication_Over_MIMO_Fading_Channels.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 7261787, "sha256": "311aeac940e4773a979be1427703d2c5836d319633062f9fc9060ef266d4e063", "ieee_document_id": "11071967", "ieee_document_url": "https://ieeexplore.ieee.org/document/11071967", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11071967", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "Semantic-Importance-Aware Reordering-Enhanced Semantic Communication System With OFDM Transmission", "authors": ["Yucheng Liu", "Chen Dong", "Haotai Liang", "Weizhi Li", "Zhicheng Bao", "Zhe Zheng", "Xiaodong Xu", "Ping Zhang"], "year": 2025, "publication_date": "2025-01-16", "doi": "10.1109/jiot.2025.3529492", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "7", "pages": "7938-7954", "abstract": "As a novel communication paradigm, semantic communication (SemCom) can greatly improve communication efficiency, which has aroused extensive research by scholars worldwide. As one of the important aspects of digital communication nowadays, how to combine channel estimation with SemCom is an important research direction. In this article, based on orthogonal frequency-division multiplexing (OFDM) communication architecture, the semantic importance-aware reordering-enhanced SemCom system (SIARE-SC) is proposed, which utilizes the inequality of semantic symbols combined with channel estimation in OFDM systems to reduce the distortion caused by channel estimation interpolation error (CEIE) and further improve the signal recovery quality. To enhance the generalizability of the system, we extend the verification of the effectiveness of SIARE-SC in various scenarios with different sources, channels, and pilot patterns. Furthermore, the importance reordering method proposed in the SIARE-SC has good applicability and effectiveness, which can be used to be compatible with other SemCom systems and has a significant suppression effect on the peak-to-average power ratio (PAPR). Meanwhile, CEIE has been considered for the first time to be included in the analysis of SemCom distortion, and mathematically derive the performance expressions of SIARE-SC under different channel and pilot pattern scenarios from three perspectives, namely, channel bandwidth ratio (CBR), signal-to-noise ratio (SNR), and CEIE, to obtain the corresponding bound of performance. The proposed SIARE-SC is shown to significantly improve semantic performance in various scenarios by conducting a large number of experimental tests.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3529492", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4406457575", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Liu_Semantic_Importance_Aware_Reordering_Enhanced_Semantic_Communication_System_With_OFDM_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 11710136, "sha256": "38838b8f75a7f157ec8068baf5e311973712d22878c79d33a01a8d5a566e42ad", "pdf_pages": 17, "pdf_title_token_match": 1.0}
{"title": "Semantic-Oriented Modulation for Wireless Communication", "authors": ["Yangfan Wang", "Xuefei Zhang", "Yao Sun", "Qimei Cui", "Xiaofeng Tao"], "year": 2025, "publication_date": "2025-04-29", "doi": "10.1109/jiot.2025.3565017", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "14", "pages": "28259-28273", "abstract": "In semantic communication (SemCom), the gain of deep learning-based joint source and channel coding (JSCC) has been proved to partly come from the analog transmission of semantic features extracted directly from the source signal. While, the current mainstream digital communication system design results in the gains almost vanishing when bit-oriented modulation (e.g., QPSK) is employed. To tackle this problem, we propose a Semantic-Oriented Modulation (SOM) method that enables direct mapping from an analog value of a semantic feature to a sequence of discrete values without bit conversion. SOM employs a hierarchical design to mitigate discretization loss and optimizes resource allocation by exploiting the varying significance of semantic features, reducing the data volume by 29.17%. We provide an analysis that reveals the impact of modulation orders and hierarchical layers, guiding the SOM’s design. Simulations in four tasks show that SOM outperforms JSCC with bit-oriented modulation, and the implementation of the Software Defined Radio (SDR) platform confirms its compatibility with existing systems and its potential for latency reduction.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3565017", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4409917125", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["JSCC", "semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Wang_Semantic_Oriented_Modulation_for_Wireless_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4660939, "sha256": "5ce3f9dbb9d93325fccfd13d0460dadf5531a46ba556409ec2831915e4673fab", "ieee_document_id": "10979950", "ieee_document_url": "https://ieeexplore.ieee.org/document/10979950", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=10979950", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "SVQ-VAE: Federated-Learning-Based Semantic-Aware Communication for Vehicular Networks", "authors": ["Zhu Jin", "Yundi Li", "Tiecheng Song", "Wen‐Kang Jia", "Xiaoqin Song"], "year": 2025, "publication_date": "2025-09-22", "doi": "10.1109/jiot.2025.3612435", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "23", "pages": "51289-51304", "abstract": "The integration of semantic communication technology into intelligent vehicular networks represents a promising research direction, as it significantly reduces data transmission volume and spectrum usage, addressing the high demands of transmitting large-scale visual information between vehicles. Existing studies typically assume that communicating parties share a common database. However, this assumption poses significant privacy risks, particularly in inter-vehicle scenarios. To address these challenges, we propose a federated learning-based semantic-aware communication for vehicular networks. In this approach, each intelligent vehicle locally trains a semantic communication model and uploads its model parameters to the edge server for aggregation. To further enhance data transmission efficiency, we introduce a semantic-aware vector quantized variational autoencoder (SVQ-VAE) architecture as the local semantic communication model. This architecture optimizes transmission by selectively compressing and quantizing only the most relevant semantic information for the task. Additionally, to address data heterogeneity among vehicles, we propose a hypernetwork-based personalized federated learning (HPFL) scheme. This approach enhances the model’s scalability and generalization by training a hypernetwork at the edge server to generate specific weight parameters for each vehicle’s semantic communication model. Simulation experiments on the CIFAR-10 and BDD100K datasets demonstrate that our proposed federated learning-based semantic-aware communication achieves superior task completion rates, semantic transmission efficiency and transmission delay compared to existing federated semantic communication architectures.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3612435", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4414404541", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "semantic transmission", "Semantic-Aware Communication", "semantic-aware communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Jin_SVQ_VAE_Federated_Learning_Based_Semantic_Aware_Communication_for_Vehicular_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2289634, "sha256": "5b9e0b05c1d7c4eb4640da2d9ce7cf30826fc22cef58922057508c9f16bf2688", "ieee_document_id": "11175013", "ieee_document_url": "https://ieeexplore.ieee.org/document/11175013", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11175013", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "Taming Learnable Codebook Design and Modulation for Digital Semantic Image Communication", "authors": ["Yingbin Zhou", "Yaping Sun", "Hongyang Du", "Guanying Chen", "Xiaodong Xu", "Hao Chen", "Ping Zhang", "Shuguang Robert Cui"], "year": 2025, "publication_date": "2025-10-08", "doi": "10.1109/jiot.2025.3619193", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "1", "pages": "473-486", "abstract": "Semantic communication employs deep learning to transmit semantically meaningful information rather than raw data, thereby improving communication efficiency. To facilitate the adaptation of continuous semantic features to digital transmission systems, vector quantization (VQ) serves as an effective approach for discretizing high-dimensional features into compact codebook indices. However, existing VQ-based systems face a critical dilemma: conventional VQ codebooks demand large index ranges to preserve fidelity, contradicting digital modulation’s need for limited discrete states to ensure noise robustness. To bridge this gap, we design a 2K image transmission framework that jointly considers codebook compactness and transmission robustness. The framework operates in two stages: In Stage 1, we devise MOC-RVQ, a multi-head ordered codebook (MOC) with residual vector quantization (RVQ) to reduce the index range while maintaining image fidelity. In Stage 2, a Swin Transformer-based noise reduction block (NRB) is integrated with feature requantization for further robust restoration. Experiments on 2K-resolution datasets demonstrate that the proposed MOC-RVQ surpasses traditional codecs like BPG, JPEG, and learnable baselines, while maintaining low transmission overhead.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3619193", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4414956244", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "reused_local_validated", "pdf_path": "papers/2025_Zhou_Taming_Learnable_Codebook_Design_and_Modulation_for_Digital_Semantic_Image_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 7536436, "sha256": "6db5ba7059c20373b44241b1aacdf1dbdbe62323e729cf68f405455f3de1d9c9", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Task-Oriented Semantic Communication With Adaptive Semantic Reconstruction Network", "authors": ["Zhu Jin", "Tiecheng Song", "Wen‐Kang Jia", "Wenbin Zou", "Xiaoqin Song"], "year": 2025, "publication_date": "2025-06-13", "doi": "10.1109/jiot.2025.3579582", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "35784-35798", "abstract": "In recent years, semantic communication has garnered significant attention for its potential to address challenges in traditional communication systems. However, in complex communication environments, semantic communication still faces challenges such as semantic information loss, low transmission efficiency, and poor adaptability. This paper proposes a novel Semantic Communication with Adaptive Semantic Reconstruction (SCASR) scheme to enhance transmission efficiency and adaptability in complex communication environments. First, a compression mechanism based on semantic importance is designed to achieve flexible and efficient semantic compression. Then, we develop an adaptive semantic reconstruction network to predict and reconstruct lost semantic information. Finally, we integrate an attention mechanism into the reconstruction network, dynamically adjusting parameter weights based on Signal-to-Noise Ratio (SNR), Semantic Compression Rate (SCR), and Packet Loss Rate (PLR) to improve reconstruction quality and adaptability. To evaluate the efficiency of SCASR, we conduct extensive simulation experiments on semantic segmentation tasks using the Cityscapes dataset. Results demonstrate that SCASR outperforms existing semantic communication and traditional schemes, offering higher Mean Intersection over Union (mIoU), and enhanced Semantic Transmission Benefit (STB).", "publisher_url": "https://doi.org/10.1109/jiot.2025.3579582", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "task-oriented communication", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4411269996", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "Semantic Transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2025_Jin_Task_Oriented_Semantic_Communication_With_Adaptive_Semantic_Reconstruction_Network.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2383039, "sha256": "0cf3378b44c10a461d0510955ed16b9fa9a1d16c8c786ea6813194fde6e3a972", "ieee_document_id": "11036522", "ieee_document_url": "https://ieeexplore.ieee.org/document/11036522", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11036522", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Toward Efficient and Privacy-Aware eHealth Systems: An Integrated Sensing, Computing, and Semantic Communication Approach", "authors": ["Yinchao Yang", "Yahao Ding", "Zhaohui Yang", "Chongwen Huang", "Zhaoyang Zhang", "Dusit Tao Niyato", "Mohammad Shikh‐Bahaei"], "year": 2025, "publication_date": "2025-10-20", "doi": "10.1109/jiot.2025.3623776", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "24", "pages": "55087-55105", "abstract": "Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3623776", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication"], "openalex_id": "https://openalex.org/W4415366473", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2025_Yang_Toward_Efficient_and_Privacy_Aware_eHealth_Systems_An_Integrated_Sensing_Computing_and_Semantic_Communication_Ap.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 717382, "sha256": "144029e2773159ac939308559784d6d787de8e691b8844fc9f24c34e6bd88ef0", "download_source": "https://arxiv.org/pdf/2510.11514v2", "arxiv_id": "2510.11514", "pdf_pages": 18, "pdf_title_token_match": 0.9167}
{"title": "Vision Transformer-Based Semantic Communications With Importance-Aware Quantization", "authors": ["Joohyuk Park", "Yongjeong Oh", "Yongjune Kim", "Yo–Seb Jeon"], "year": 2025, "publication_date": "2025-06-17", "doi": "10.1109/jiot.2025.3580597", "venue": "IEEE Internet of Things Journal", "volume": "12", "issue": "17", "pages": "35662-35677", "abstract": "Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features through wireless channels. However, most existing studies rely on end-to-end (E2E) training of neural-type encoders and decoders to ensure effective transmission of these semantic features. To enable semantic communications without relying on E2E training, this paper presents a vision transformer (ViT)-based semantic communication system with importance-aware quantization (IAQ) for wireless image transmission. The core idea of the presented system is to leverage the attention scores of a pretrained ViT model to quantify the importance levels of image patches. Based on this idea, our IAQ framework assigns different quantization bits to image patches based on their importance levels. This is achieved by formulating a weighted quantization error minimization problem, where the weight is set to be an increasing function of the attention score. Then, an optimal incremental allocation method and a low-complexity water-filling method are devised to solve the formulated problem. Our framework is further extended for realistic digital communication systems by modifying the bit allocation problem and the corresponding allocation methods based on an equivalent binary symmetric channel (BSC) model. Simulations on single-view image classification, multi-view image classification, and single-object detection tasks demonstrate that our IAQ framework outperforms conventional image compression methods under both error-free and realistic communication scenarios.", "publisher_url": "https://doi.org/10.1109/jiot.2025.3580597", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W4411358807", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication", "wireless image transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2025_Park_Vision_Transformer_Based_Semantic_Communications_With_Importance_Aware_Quantization.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3437689, "sha256": "ae5256fbef8108ee5fb775dbda384b15b90ec49403568514e7d9b1ea0a616458", "download_source": "https://arxiv.org/pdf/2412.06038v1", "arxiv_id": "2412.06038", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "A Coarse-to-Fine Multi-Scale Fusion Receiver for Semantic Communication Against Semantic Jamming", "authors": ["Yanchao Lei", "Ying Li", "Xiaofeng Zhang", "Zhaoji Zhang", "Guanghui Song"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/jiot.2026.3738108", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3738108", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7214789820", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Lei_A_Coarse_to_Fine_Multi_Scale_Fusion_Receiver_for_Semantic_Communication_Against_Semantic_Jamming.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 25140427, "sha256": "5ae18e41e05028a791ef8b925a20f8cf16404730eb9628a7f01cd332f1623751", "ieee_document_id": "11713410", "ieee_document_url": "https://ieeexplore.ieee.org/document/11713410", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11713410", "pdf_pages": 16, "pdf_title_token_match": 1.0}
{"title": "A Robust Digital Semantic Communication Framework via Channel-wise Partitioning and Non-uniform Quantization", "authors": ["Xiao Yang", "Shuo Ma", "Yong Qi Liang", "Guangming Shi"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/jiot.2026.3725343", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3725343", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7203796423", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Yang_A_Robust_Digital_Semantic_Communication_Framework_via_Channel_wise_Partitioning_and_Non_uniform_Quantization.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4907767, "sha256": "03f81214a2eea122ad41bf3cbcf3046ea4690e44834228c967283e255d964d75", "ieee_document_id": "11659554", "ieee_document_url": "https://ieeexplore.ieee.org/document/11659554", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11659554", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "A Robust Quantized Speech Semantic Communication System Based on ASR Perception Loss and Deep Channel Estimation", "authors": ["Haoyu Yue", "Weikai Xu", "Mingzhang Zhou", "Haixin Sun"], "year": 2026, "publication_date": "2026-07-03", "doi": "10.1109/jiot.2026.3709950", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "17", "pages": "41162-41176", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3709950", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "deep joint source-channel coding", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7167231033", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Yue_A_Robust_Quantized_Speech_Semantic_Communication_System_Based_on_ASR_Perception_Loss_and_Deep_Channel_Estimation.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 6080438, "sha256": "1589b13bdb9da304a644d2886174e0427302d91bb0356f2b2c6d638618b36892", "ieee_document_id": "11594877", "ieee_document_url": "https://ieeexplore.ieee.org/document/11594877", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11594877", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Adaptive Reliable Semantic Transmission Strategy for Mobile Multi-User Multi-Tasking", "authors": ["Ping Xie", "Shuaidi Ma", "Meiyi Yang", "Meiwen Li", "Rundong Lin", "Yu Zhang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/jiot.2026.3737279", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3737279", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W7214178668", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Xie_Adaptive_Reliable_Semantic_Transmission_Strategy_for_Mobile_Multi_User_Multi_Tasking.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2076222, "sha256": "0dc3a01c4b544dae6b3dad0a9af71a4cfffbce0dae02b18707795d49d9cabacc", "ieee_document_id": "11707270", "ieee_document_url": "https://ieeexplore.ieee.org/document/11707270", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11707270", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "Adaptive Symbol and Power Loading for OFDM-Based Underwater Wireless Optical Semantic Communications", "authors": ["Jie Xu", "Zhitong Huang", "Yi Gao", "Hongcheng Qiu", "Yuefeng Ji"], "year": 2026, "publication_date": "2026-02-10", "doi": "10.1109/jiot.2026.3663138", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "8", "pages": "17643-17657", "abstract": "Underwater wireless optical communication (UWOC) is a key technology for supporting information transmission in the Internet of Underwater Things (IoUT). Recently, semantic communication has been introduced into UWOC systems to enhance transmission efficiency and robustness. However, existing underwater wireless optical semantic communication (UWOSC) systems overlook the bandwidth limitation imposed by practical optoelectronic devices, which hinders high-speed semantic information transmission. In this paper, we introduce orthogonal frequency division multiplexing (OFDM) modulation into the UWOSC system for the first time, and design a novel OFDM-based UWOSC system with symbol and power loading (SPL). In the proposed system, shifted-window-based hierarchical vision transformer blocks are employed to extract semantic features and encode them into frequency-domain baseband symbols for OFDM transmission. Furthermore, an SPL scheme, comprising an entropy model-driven symbol reordering strategy and a lightweight power allocation network, is designed to adaptively allocate subcarriers and power based on semantic importance and channel conditions. Experiments conducted on an emulated UWOC platform demonstrate that the proposed OFDM-based UWOSC system achieves superior performance compared with baseline schemes under high transmission bandwidth scenarios. Moreover, the introduction of the SPL module further improves semantic spectrum efficiency, effectively mitigating the adverse impact of low-pass characteristic inherent in bandwidth-limited UWOSC systems. These results demonstrate that the proposed system has strong potential to enable high-speed semantic communication in the IoUT.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3663138", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "semantic transmission", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7128488577", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Xu_Adaptive_Symbol_and_Power_Loading_for_OFDM_Based_Underwater_Wireless_Optical_Semantic_Communications.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5147932, "sha256": "777ef57c6d3a2a0cebcfe02cb3f0ce5bd6feebc5b56348249e19773e270ce485", "ieee_document_id": "11389779", "ieee_document_url": "https://ieeexplore.ieee.org/document/11389779", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11389779", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Airtalking: Aerial D2D for Multi-UAV Systems Based on Semantic Communication", "authors": ["Jianwen Xu", "Kaoru Ota", "Mianxiong Dong"], "year": 2026, "publication_date": "2026-05-22", "doi": "10.1109/jiot.2026.3696083", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "17", "pages": "38392-38404", "abstract": "Unmanned aerial vehicles (UAVs) have become indispensable tools in logistics, mapping, and disaster response, etc. However, effective cooperation among multiple UAVs remains challenging due to limited direct communication and energy constraints in aerial environments. Conventional device-to-device (D2D) frameworks on the ground are insufficient to cope with the complexity in UAV-assisted networks, such as frequent topology variation, interference fluctuation, and battery power. To overcome these limitations, this paper proposes a semantic-aware cellular D2D communication architecture, namedAirtalking, which integrates semantic encoding and decoding to enable efficient UAV-to-UAV (U2U) data exchange. Unlike traditional bit-level transmission, semantic communication focuses on delivering essential information, thereby reducing payload size and improving time and energy efficiency. We design algorithms to balance latency and energy consumption and simulate with different scheduling policies under varying flight conditions. The results show that the proposed aerial U2U framework can improve communication stability and efficiency, demonstrating the feasibility of deploying semantic communication in practical multi-UAV systems.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3696083", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7162143003", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Xu_Airtalking_Aerial_D2D_for_Multi_UAV_Systems_Based_on_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2428314, "sha256": "c4b7706bfe8e722457a66e733767f3c51b6bc11efe714c21c169e4159a8e00b3", "ieee_document_id": "11534223", "ieee_document_url": "https://ieeexplore.ieee.org/document/11534223", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11534223", "pdf_pages": 13, "pdf_title_token_match": 1.0}
{"title": "Bidirectional Motion-Enhanced Semantic Communication for Wireless Video Transmission", "authors": ["Zhenguo Zhang", "Qianqian Yang", "Shibo He", "Zhiguo Shi"], "year": 2026, "publication_date": "2026-01-23", "doi": "10.1109/jiot.2026.3657245", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "8", "pages": "15607-15620", "abstract": "With the increasing proliferation of Ultra-High-Definition (UHD) videos, the demand for efficient video transmission schemes to alleviate network congestion is growing. In this paper, we propose a bi-directional motion enhanced semantic communication (SemCom) system for efficient and robust video transmission. In particular, we introduce a bi-directional motion estimation module to capture inter-frame differences caused by camera movements, where the obtained forward and backward motion vectors are combined with the residual information to generate motion-compensated frames. We also introduce a predicted feature module to discard semantically redundant features, prioritizing crucial semantic-related content. Leveraging information from previously reconstructed frames, the frame prediction module refines predicted frames with the assistance of the motion compensation module. To enhance the system’s robustness to channel noise, we propose a noise attention module that assigns varying importance weights to the extracted features under different channel conditions. Experimental results show that our proposed method outperforms existing deep learning (DL)-based approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) and multi-scale structural similarity index measure (MS-SSIM) performance by an average of 0.56 dB and 0.0024 over an additive white Gaussian noise channel for different schemes. When employing the same compression ratio, our method achieves an average gain of 0.637 dB in PSNR and 0.0038 in MS-SSIM over the slow Rayleigh fading channel.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3657245", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "wireless image transmission", "wireless video transmission", "semantic modulation"], "openalex_id": "https://openalex.org/W7125595645", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication", "Wireless Video Transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_ieee_validated", "pdf_path": "papers/2026_Zhang_Bidirectional_Motion_Enhanced_Semantic_Communication_for_Wireless_Video_Transmission.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3744131, "sha256": "48255833c905279affb8798312543d9c283f460de7df31297e452c8f27ddd25f", "ieee_document_id": "11362392", "ieee_document_url": "https://ieeexplore.ieee.org/document/11362392", "ieee_pdf_url": "https://ieeexplore.ieee.org/stampPDF/getPDF.jsp?tp=&arnumber=11362392", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Contrastive Learning and Adversarial Disentanglement for Privacy-Aware Task-Oriented Semantic Communication", "authors": ["Omar Erak", "Omar Alhussein", "Wen Tong"], "year": 2026, "publication_date": "2026-03-12", "doi": "10.1109/jiot.2026.3672921", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "12", "pages": "25948-25965", "abstract": "Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only information relevant to a specific task is communicated. This is particularly important in 6G-enabled Internet of Things (6G-IoT) scenarios, where bandwidth constraints, latency requirements, and data privacy are critical. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and suboptimal performance. To address this, we propose an information-bottleneck inspired method, named CLAD (contrastive learning and adversarial disentanglement). CLAD utilizes contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the absence of reliable and reproducible methods to quantify the minimality of encoded feature vectors, we introduce the Information Retention Index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input. The IRI reflects how minimal and informative the representation is, making it highly relevant for privacy-preserving and bandwidth-efficient 6G-IoT systems. Extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of semantic extraction, task performance, privacy preservation, and IRI, making it a promising building block for responsible, efficient and trustworthy 6G-IoT services.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3672921", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "task-oriented communication", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7135011214", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://doi.org/10.1109/jiot.2026.3672921", "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2026_Erak_Contrastive_Learning_and_Adversarial_Disentanglement_for_Privacy_Aware_Task_Oriented_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 5731138, "sha256": "fd909b55960525e454d46009fd3f52450d09bc2b72c352bd33576e955be2bd9b", "download_source": "https://arxiv.org/pdf/2410.22784v3", "arxiv_id": "2410.22784", "pdf_pages": 15, "pdf_title_token_match": 1.0}
{"title": "Feature Importance-Aware Deep Joint Source-Channel Coding for Computationally Efficient and Adjustable Image Transmission", "authors": ["H M Choi", "Daewon Seo"], "year": 2026, "publication_date": "2026-04-03", "doi": "10.1109/jiot.2026.3680582", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "12", "pages": "27893-27911", "abstract": "Recent advances in deep learning-based joint source–channel coding (deepJSCC) have substantially improved communication performance, but their growing computational cost hinders practical deployment. Moreover, certain applications require the ability to dynamically adapt computational complexity. To address these issues, we propose a Feature Importance-Aware deepJSCC (FAJSCC) model for image transmission that is both computationally efficient and adjustable. FAJSCC employs axis-dimension specialized computation, which performs efficient operations individually for each spatial and channel axis, significantly reducing computational cost while representing features effectively. It further incorporates selective deformable self-attention, which applies self-attention only to selected and adaptively adjusted features, leveraging the importance and relations of input features to efficiently capture complex feature correlations. Another key feature of FAJSCC is that the number of selected important areas can be controlled separately by the encoder and the decoder, depending on the available computational budget. It makes FAJSCC the first deepJSCC architecture to allow independent adjustment of encoder and decoder complexity within a single trained model. Experimental results show that FAJSCC achieves superior image transmission performance under various channel conditions while requiring less computational complexity than recent state-of-the-art models. Furthermore, experiments independently varying the encoder and decoder’s computational resources reveal, for the first time in the deepJSCC literature, that understanding the meaning of noisy features in the decoder demands the greatest computational cost. The code is publicly available at github.com/hansung-choi/FAJSCCv2.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3680582", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "deep joint source-channel coding", "joint source-channel coding", "semantic transmission", "semantic-aware channel coding", "wireless image transmission", "wireless video transmission"], "openalex_id": "https://openalex.org/W4416117409", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2504.04758", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Deep Joint Source-Channel", "deepJSCC"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2026_Choi_Feature_Importance_Aware_Deep_Joint_Source_Channel_Coding_for_Computationally_Efficient_and_Adjustable_Image_Tra.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3005225, "sha256": "dccd014841eaabcfbc74d0db23ed6ebfcc1c1f0349673a6879b6193ae7283749", "download_source": "https://arxiv.org/pdf/2504.04758", "pdf_pages": 19, "pdf_title_token_match": 1.0}
{"title": "Low-altitude Multi-UAV-assisted Data Collection and Semantic Forwarding for Post-Disaster Relief", "authors": ["Xiaoya Zheng", "Geng Sun", "Jiahui Li", "Jiacheng Wang", "Weijie Yuan", "Qingqing Wu", "Abbas Jamalipour"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/jiot.2026.3729628", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "The low-altitude economy (LAE) is an emerging economic paradigm which fosters integrated development across multiple fields. As a pivotal component of the LAE, low-altitude uncrewed aerial vehicles (UAVs) can restore communication by serving as aerial relays between the post-disaster areas and remote base stations (BSs). However, conventional approaches face challenges from vulnerable long-distance links between the UAVs and remote BSs, and data bottlenecks arising from massive data volumes and limited onboard UAV resources. In this work, we investigate a low-altitude multi-UAV-assisted data collection and semantic forwarding network, in which multiple UAVs collect data from ground users, form clusters, perform intra-cluster data aggregation with semantic extraction, and then cooperate as virtual antenna array (VAAs) to transmit the extracted semantic information to a remote BS via collaborative beamforming (CB). We formulate a data collection and semantic forwarding multi-objective optimization problem (DCSFMOP) that jointly maximizes both the user and semantic transmission rates while minimizing UAV energy consumption. The formulated DCSFMOP is a mixed-integer nonlinear programming (MINLP) problem that is inherently NP-hard and characterized by dynamically varying decision variable dimensionality. To address these challenges, we propose a large language model-enabled alternating optimization approach (LLM-AOA), which effectively handles the complex search space and variable dimensionality by optimizing different subsets of decision variables through tailored optimization strategies. Simulation results demonstrate that LLM-AOA outperforms AOA by approximately 26.8\\% and 22.9\\% in transmission rate and semantic rate, respectively.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3729628", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7125473597", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2601.16146", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "medium", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["semantic transmission"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2026_Zheng_Low_altitude_Multi_UAV_assisted_Data_Collection_and_Semantic_Forwarding_for_Post_Disaster_Relief.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 4514971, "sha256": "d8d28b9718920b0705916270e535035b73322c608fac7fd4f147d1eabb42dc9a", "download_source": "https://arxiv.org/pdf/2601.16146", "pdf_pages": 18, "pdf_title_token_match": 1.0}
{"title": "On the Rate-Distortion-Complexity Tradeoff for Semantic Communication", "authors": ["Jingxuan Chai", "Yong Liang Xiao", "Guangming Shi"], "year": 2026, "publication_date": "2026-05-01", "doi": "10.1109/jiot.2026.3689652", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "14", "pages": "31768-31781", "abstract": "Semantic communication is a novel communication paradigm that focuses on conveying the user's intended meaning rather than the bit-wise transmission of source signals. One of the key challenges is to effectively represent and extract the semantic meaning of any given source signals. While deep learning (DL)-based solutions have shown promising results in extracting implicit semantic information from a wide range of sources, existing work often overlooks the high computational complexity inherent in both model training and inference for the DL-based encoder and decoder. To bridge this gap, this paper proposes a rate-distortion-complexity (RDC) framework which extends the classical rate-distortion theory by incorporating the constraints on semantic distance, including both the traditional bit-wise distortion metric and statistical difference-based divergence metric, and complexity measure, adopted from the theory of minimum description length and information bottleneck. We derive the closed-form theoretical results of the minimum achievable rate under given constraints on semantic distance and complexity for both Gaussian and binary semantic sources. Our theoretical results show a fundamental three-way tradeoff among achievable rate, semantic distance, and model complexity. Extensive experiments on real-world image and video datasets validate this tradeoff and further demonstrate that our information-theoretic complexity measure effectively correlates with practical computational costs, guiding efficient system design in resource-constrained scenarios.", "publisher_url": "https://doi.org/10.1109/jiot.2026.3689652", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7160040811", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2602.14481", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "related", "screening_priority": "high", "screening_reason": "network/theory/control layer directly optimized around a semantic communication link", "matched_topic_terms": ["Semantic communication", "Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2026_Chai_On_the_Rate_Distortion_Complexity_Tradeoff_for_Semantic_Communication.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 3361389, "sha256": "67edec9eedd9384c7d0f12d22a0ad3a3999149c181c1d05f7f62c6dbe044d52c", "download_source": "https://arxiv.org/pdf/2602.14481", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Rate-Splitting Multiple Access-Enabled Probabilistic Semantic Communication in UAV Networks", "authors": ["Sicheng Wang", "Tiankui Zhang", "Xu Gan", "Wenjun Xu"], "year": 2026, "publication_date": "2026-07-31", "doi": "10.1109/jiot.2026.3718639", "venue": "IEEE Internet of Things Journal", "volume": "13", "issue": "18", "pages": "43510-43524", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3718639", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "openalex_id": "https://openalex.org/W7171986703", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_arxiv_validated", "pdf_path": "papers/2026_Wang_Rate_Splitting_Multiple_Access_Enabled_Probabilistic_Semantic_Communication_in_UAV_Networks.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 925806, "sha256": "e11e3f2565ad60d78663f8f067d1b7b5f0f7245f84d10570cf3c48b96c93d22b", "download_source": "https://arxiv.org/pdf/2606.30993v1", "arxiv_id": "2606.30993", "pdf_pages": 14, "pdf_title_token_match": 1.0}
{"title": "Unequal Error Protection for Digital Semantic Communication with Channel Coding", "authors": ["Seonjung Kim", "Yongjeong Oh", "Yongjune Kim", "Namyoon Lee", "Yo-Seb Jeon"], "year": 2026, "doi": "10.1109/jiot.2026.3740131", "venue": "IEEE Internet of Things Journal", "volume": "", "issue": "", "pages": "1-1", "abstract": "", "publisher_url": "https://doi.org/10.1109/jiot.2026.3740131", "sources": ["Crossref keyword search"], "matched_queries": ["semantic communication", "semantic communications", "deep joint source-channel coding", "joint source-channel coding", "goal-oriented communication", "semantic transmission", "semantic-aware communication", "digital semantic communication", "semantic communication vector quantization", "VQ semantic communication", "discrete semantic communication", "semantic communication codebook", "semantic communication quantization", "digital JSCC semantic communication", "semantic-aware channel coding", "index error semantic communication", "bit error semantic communication", "OFDM digital semantic communication", "product quantization semantic communication", "semantic modulation"], "record_type": "journal-article", "screening_status": "core", "screening_priority": "high", "screening_reason": "direct semantic/DeepJSCC representation, transmission, receiver, or end-to-end system", "matched_topic_terms": ["Semantic Communication"], "publication_year_basis": "formal IoTJ volume year when available; otherwise current Early Access year", "pdf_status": "downloaded_open_version_validated", "pdf_path": "papers/2026_Kim_Unequal_Error_Protection_for_Digital_Semantic_Communication_with_Channel_Coding.pdf", "pdf_valid": true, "pdf_header_ok": true, "pdf_bytes": 2028898, "sha256": "4e058e8fdb6217409d8570abf53c0ca3dde11356a8a8b8e97896dc66058ec755", "download_source": "https://arxiv.org/pdf/2508.03381", "pdf_pages": 14, "pdf_title_token_match": 1.0}