{"title": "Economics of Semantic Communication System: An Auction Approach", "authors": ["Zi Qin Liew", "Hongyang Du", "Wei Yang Bryan Lim", "Zehui Xiong", "Dusit Tao Niyato", "Chunyan Miao", "Dong In Kim"], "year": 2023, "publication_date": "2023-05-22", "doi": "10.1109/tvt.2023.3278467", "venue": "IEEE Transactions on Vehicular Technology", "volume": "72", "issue": "10", "pages": "13559-13574", "abstract": "Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meanings of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called “semantic model trading”. Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called “semantic information trading”. To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this article, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them to achieve higher utilities than the baseline method.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3278467", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4377235105", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2208.05040", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Joint User Association and Bandwidth Allocation in Semantic Communication Networks", "authors": ["Le Xia", "Yao Sun", "Dusit Tao Niyato", "Xiaoqian Li", "Muhammad Ali Imran"], "year": 2023, "publication_date": "2023-09-22", "doi": "10.1109/tvt.2023.3318510", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "2", "pages": "2699-2711", "abstract": "Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this article investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3318510", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4386952236", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2212.14142", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "Knowledge-Aware Semantic Communication System Design and Data Allocation", "authors": ["Sachin Kadam", "Dong In Kim"], "year": 2023, "publication_date": "2023-11-16", "doi": "10.1109/tvt.2023.3333350", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "4", "pages": "5755-5769", "abstract": "The recent emergence of 6G raises the challenge of increasing the transmission data rate even further in order to overcome the Shannon limit. Traditional communication methods fall short of the 6G goals, paving the way for Semantic Communication (SemCom) systems that have applications in the metaverse, healthcare, economics, etc. In SemCom systems, only the relevant keywords from the data are extracted and used for transmission. In this article, we design an auto-encoder and auto-decoder that only transmit these keywords and, respectively, recover the data using the received keywords and the shared knowledge. This SemCom system is used in a setup in which the receiver allocates various categories of the same dataset collected from the transmitter, which differ in size and accuracy, to a number of users. This scenario is formulated using an optimization problem called the data allocation problem (DAP). We show that it is NP-complete and propose a greedy algorithm to solve it. Using simulations, we show that the proposed methods for SemCom system design outperform state-of-the-art methods in terms of average number of words per sentence for a given accuracy, and that the proposed greedy algorithm solution of the DAP performs significantly close to the optimal solution.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3333350", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4388720088", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2301.03468", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "LDPC Coded Compressive Sensing for Joint Source-Channel Coding in Wireless Sensor Networks", "authors": ["Jue Chen", "Shuai Shao", "Tsang-Yi Wang", "Jwo-Yuh Wu", "Chih-Peng Li", "Soon Xin Ng", "Robert G. Maunder", "Lajos Hanzo"], "year": 2023, "doi": "10.1109/tvt.2022.3212025", "venue": "IEEE Transactions on Vehicular Technology", "volume": "72", "issue": "2", "pages": "2145-2160", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2022.3212025", "sources": ["Crossref keyword search"], "matched_queries": ["deep joint source-channel coding", "joint source-channel coding", "semantic-aware channel coding", "joint source channel coding image transmission", "joint source channel coding video transmission"], "record_type": "journal-article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding"]}
{"title": "Mechanism Design for Semantic Communication in UAV-Assisted Metaverse: A Combinatorial Auction Approach", "authors": ["Zi Qin Liew", "Minrui Xu", "Wei Yang Bryan Lim", "Zehui Xiong", "Dusit Tao Niyato", "Dong In Kim"], "year": 2023, "publication_date": "2023-09-19", "doi": "10.1109/tvt.2023.3317069", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "2", "pages": "2236-2251", "abstract": "Metaverse enables users to experience a virtual space that resembles the real world. In the Metaverse, virtual service providers (VSPs) maintain digital twins (DTs) of real-world entities to offer innovative services such as virtual conferences and tourism. However, to provide a realistic experience, VSPs need to collect up-to-date and relevant data, which is latency-sensitive and resource-intensive, from edge devices such as unmanned aerial vehicles (UAVs). To address this challenge, we propose a semantic communication framework that leverages extraction and trading of scene graphs to reduce communication costs and motivate data transfer for Metaverse services. In the framework, we propose a semantic communication system where data for building DTs is encoded as semantic information, such as scene graphs. VSPs can use these graphs to build or update their DTs. To facilitate data trading between VSPs and edge nodes, we introduce a combinatorial auction-based incentive mechanism where we model the valuation of semantic data based on relatedness, freshness, and semantic values. We measure the relatedness based on the similarity between targeted objects of VSPs and the objects present in the real-world data. We use the age of information (AoI) to quantify data freshness and evaluate the semantic value using the information richness of the scene graphs. Simulation results show that our system can maintain the model's mean recall performance using only 12.5% of features during transmission. Our mechanism increases the average update frequency effectively. The theoretical analysis proves that the proposed mechanism guarantees the desired properties of individual rationality and incentive compatibility.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3317069", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4386869667", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Rethinking Estimation Rate for Wireless Sensing: A Rate-Distortion Perspective", "authors": ["Fuwang Dong", "Fan Liu", "Shihang Lu", "Yifeng Xiong"], "year": 2023, "publication_date": "2023-07-24", "doi": "10.1109/tvt.2023.3298005", "venue": "IEEE Transactions on Vehicular Technology", "volume": "72", "issue": "12", "pages": "16876-16881", "abstract": "Wireless sensing has been recognized as a key enabling technology for numerous emerging applications. For decades, the sensing performance was mostly evaluated from a reliability perspective, with the efficiency aspect widely unexplored. Motivated from both backgrounds of rate-distortion theory and optimal sensing waveform design, a novel efficiency metric, namely, the sensing estimation rate (SER), is defined to unify the information- and estimation- theoretic perspectives of wireless sensing. Specifically, the active sensing process is characterized as a virtual lossy data transmission through non-cooperative joint source-channel coding. The bounds of SER are analyzed based on the data processing inequality, followed by a detailed derivation of achievable bounds under the special cases of the Gaussian linear model (GLM) and semi-controllable GLM. As for the intractable non-linear model, a computable upper bound is also given in terms of the Bayesian Cramér-Rao bound (BCRB). Finally, we show the rationality and effectiveness of the SER defined by comparing to the related works.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3298005", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4385222745", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2303.11857", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["joint source-channel coding"]}
{"title": "Semantic Communication-Based Dynamic Resource Allocation in D2D Vehicular Networks", "authors": ["Jiawei Su", "Zhixin Liu", "Yuan-ai Xie", "Kai Ma", "Hongyang Du", "Jiawen Kang", "Dusit Tao Niyato"], "year": 2023, "publication_date": "2023-03-16", "doi": "10.1109/tvt.2023.3257770", "venue": "IEEE Transactions on Vehicular Technology", "volume": "72", "issue": "8", "pages": "10784-10796", "abstract": "The semantic communication mechanism enables wireless devices in vehicular networks to communicate more effectively with the semantic meaning. However, in high-dynamic vehicular networks, the transmission of semantic information faces challenges in terms of reliability and stability. To address these challenges, a long-term robust resource allocation scheme is proposed under the Device-to-Device (D2D) vehicular (D2D-V) networks, where multiple performance indicators (user satisfaction, queue stability, and communication delay) are considered. Due to the sophisticated probabilistic form with consideration of channel fluctuations, the Bernstein approximation is introduced to acquire the deterministic constraint more efficiently. The robust resource allocation problem is proposed and separated into two independent subproblems by the Lyapunov optimization method, which includes semantic access control in the application layer and power control in the physical layer. After that, the successive convex approximation method and Karush-Kuhn-Tucher conditions are adopted to solve the subproblems, thereby proposing a robust resource allocation algorithm. The simulations reveal the trade-off relationship between user satisfaction, queue stability, and communication delay, which is on the premise of meeting the user SINR requirement. Moreover, the simulations also prove the necessity of considering channel uncertainty in high-speed mobile vehicular communication scenarios.", "publisher_url": "https://doi.org/10.1109/tvt.2023.3257770", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4327643814", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Understand-Before-Talk (UBT): A Semantic Communication Approach to 6G Networks", "authors": ["Shiva Raj Pokhrel", "Jinho Choi"], "year": 2023, "doi": "10.1109/tvt.2022.3219363", "venue": "IEEE Transactions on Vehicular Technology", "volume": "72", "issue": "3", "pages": "3544-3556", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2022.3219363", "sources": ["Crossref keyword search"], "matched_queries": ["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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "record_type": "journal-article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "A Semantic Communication System for Point Cloud", "authors": ["Xiaoyi Liu", "Haotai Liang", "Zhicheng Bao", "Chen Dong", "Xiaodong Xu"], "year": 2024, "publication_date": "2024-09-12", "doi": "10.1109/tvt.2024.3456099", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "1", "pages": "894-910", "abstract": "Point cloud, as a 3D representation, finds wide applications in domains such as autonomous driving, virtual reality (VR), and augmented reality (AR). However, traditional communication systems are not well-suited for large-scale point cloud data transmissions, as such systems operate at the bit level without leveraging the inherent semantic information of the point cloud. This paper introduces a point cloud-based semantic communication system (PCSC) that leverages AI-based encoding techniques to extract semantic information from the point cloud. Furthermore, joint source-channel coding (JSCC) technology is employed to overcome noise channel distortion and address the “cliff effect” prevalent in traditional communication methods. Additionally, the proposed system achieves controllable coding rates without requiring extensive network fine-tuning. By analyzing the significance of the encoded semantic vector, the method discards semantically-unimportant information, enhancing transmission efficiency. Moreover, the PCSC is integrated with the recently proposed model division multiple access (MDMA) technology, resulting in a multi-user point cloud MDMA transmission system (M-PCSC). Experimental results demonstrate that the proposed method surpasses traditional approaches by 10 dB under the PSNR D1 and PSNR D2 metrics while effectively mitigating the “cliff effect” in transmission.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3456099", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4402475090", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["joint source-channel coding", "JSCC", "semantic communication", "Semantic Communication"]}
{"title": "An Efficient Federated Learning Framework for Training Semantic Communication Systems", "authors": ["Loc X. Nguyen", "Huy Q. Le", "Ye Lin Tun", "Pyae Sone Aung", "Yan Kyaw Tun", "Zhu Han", "Choong Seon Hong"], "year": 2024, "publication_date": "2024-05-15", "doi": "10.1109/tvt.2024.3401140", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "10", "pages": "15872-15877", "abstract": "Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions of a readily accessible data source, where in practice, data is mainly created on the client side. This circumstance limits the transmission of data due to privacy concerns, which is necessary for conventional centralized training schemes. To address this challenge, we explore semantic communication in a federated learning (FL) setting, which can harness the data of clients without compromising their privacy. Additionally, we design our system to lower the communication overhead by reducing the quantity of information delivered in each FL training round. This not only allows us to save significant bandwidth for resource-constrained devices but also reduces overall network traffic. Finally, we introduce a mechanism to aggregate the global model based on the client's performance, which we have referred to as FedLol. Extensive simulation results demonstrate the effectiveness of our proposed technique compared to baseline methods.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3401140", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4396918245", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2310.13236", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "An Image Adaptive Rate Mechanism in Semantic Communication for Image Endogenous Semantics", "authors": ["Guangming Shi", "Haixiong Li", "Dahua Gao", "Minxi Yang", "Yubo Dong"], "year": 2024, "publication_date": "2024-05-03", "doi": "10.1109/tvt.2024.3396426", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "9", "pages": "13425-13439", "abstract": "Despite deep learning's progress in semantic communication, traditional fixed-length encoding does not adequately address the variable complexity of semantic content, often leading to loss of critical nuances and reduced communication accuracy. Current methods also introduce unnecessary redundancy, compromising transmission efficiency. Addressing these challenges, our work introduces an innovative adaptive rate encoding mechanism that captures the intrinsic semantics of images and fine-tunes the coding rate based on semantic interconnection probability. We employ a cross-attention model to construct a layered semantic probability graph parsed into a hierarchical semantic tree, which represents the probabilistic relationships of image semantics and unravels the latent semantic structure. This not only delineates the image's semantic architecture but also enables our adaptive encoding to dynamically allocate resources, minimizing redundancy and enhancing efficiency. Our experiments confirm that our approach provides a more judicious bit allocation to complex image features and allocates more bits to semantically rich features while achieving superior compression of simpler content. The proposed method not only improves upon existing semantic fidelity metrics but also reduces the bit demand for transmitting complex images. Our adaptive encoding strategy represents a significant stride in leveraging the endogenous semantic information of images for more accurate and efficient communication.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3396426", "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", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4396605285", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "CSBA: Covert Semantic Backdoor Attack Against Intelligent Connected Vehicles", "authors": ["Xiaodong Xu", "Yue Chen", "Bizhu Wang", "Zhiqiang Bian", "Shujun Han", "Chen Dong", "Chen Sun", "Wenqi Zhang", "Lexi Xu", "Ping Zhang"], "year": 2024, "publication_date": "2024-07-16", "doi": "10.1109/tvt.2024.3427713", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "11", "pages": "17923-17928", "abstract": "Semantic communication (SemCom) can reduce data traffic for intelligent connected vehicles (ICVs), given the limited wireless spectrum available. However, it is important to recognize that deep learning-based SemCom is vulnerable to backdoor attacks, which pose significant security risks to ICVs. Therefore, it is crucial to investigate these security risks before integrating SemCom into ICVs. To this end, this study introduces a novel backdoor attack known as Covert Semantic Backdoor Attack (CSBA), specifically designed for SemCom-enabled ICVs. Unlike existing backdoor attack techniques that rely on noticeable triggers, CSBA analyzes the self-contained semantics in transmitted images to determine if they contain the target semantic required for initiating a backdoor attack. Moreover, in the event of an attack by CSBA, the target semantics disappear from the recovered image while the rest of the image remains unchanged, ensuring that the attack remains invisible. Experimental results confirm the effectiveness and the stealthiness of the proposed CSBA schemes across various wireless channel conditions and attack ratios.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3427713", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4400680555", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["Semantic communication"]}
{"title": "Deep Joint CSI Feedback and Multiuser Precoding for MIMO OFDM Systems", "authors": ["Yiran Guo", "Wei Ren Chen", "Jialong Xu", "Lun Li", "Bo Ai"], "year": 2024, "publication_date": "2024-08-30", "doi": "10.1109/tvt.2024.3452409", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "1", "pages": "1730-1735", "abstract": "The design of precoding plays a crucial role in achieving a high downlink sum rate in multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In this correspondence, we propose a deep learning based joint CSI feedback and multiuser precoding method in frequency division duplex systems, aiming at maximizing the downlink sum-rate performance in an end-to-end manner. Specifically, the eigenvectors of the CSI matrix are compressed using deep joint source-channel coding techniques. This compression method enhances the resilience of the feedback CSI against degradation in the feedback channel. A joint multiuser precoding module and a power allocation module are designed to adjust the precoding direction and the precoding power for users based on the feedback CSI. Experimental results demonstrate that the downlink sum-rate can be significantly improved by using the proposed method, especially in scenarios with low signal-to-noise ratio and low feedback overhead.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3452409", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4402040357", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2404.16289", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["deep joint source-channel"]}
{"title": "End-to-End Optimization of Semantic Communication Systems: Joint Source-Channel-Host-Tasks", "authors": ["Siting Lv", "Xiaohui Li", "Jiawen Liu", "Mingli Shi", "Xingbo Chen"], "year": 2024, "publication_date": "2024-12-24", "doi": "10.1109/tvt.2024.3521948", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "5", "pages": "7580-7593", "abstract": "In images semantic communication, the efficacy of communication systems is intricately influenced by multiple factors, including images compression rates, semantic noise, channel noise, etc. These factors exhibit intricate interdependencies and pose formidable challenges when it comes to their quantitative assessment through mathematical formulations. Furthermore, semantic communication tailored to various image transmission tasks imposes different requirements on the system, necessitating a holistic consideration of these influential elements. This paper presents a method devised to optimize images semantic communication systems by joining the facets of Source-Channel-Host-Task (JSCHT). The primary objective of proposing this approach is to ensure the successful execution of images communication tasks. Initially, we propose an adaptive scheme for semantic compression. This scheme is proposed to tackle the intricate problem of optimally aligning semantic information with channel states. This scheme establishes a dynamic linkage between the source and the channel, adjusting the degree of semantic compression in response to signal-to-noise ratios (SNRs). As a consequence, the system's flexibility is heightened. Subsequently, we architect an end-to-end codec network grounded in deep learning (DL) principles to tackle the challenge of accommodating SNRs changes. The differentiable channel model is integrated into this framework as a network layer, actively participating in the processes of backpropagation and gradient computation. Through the implementation of end-to-end training by joint source-dynamic noise channel-host, system robustness undergoes pronounced enhancement. In addition, within the semantic communication environment for various image transmission tasks, an on-demand feature extraction module is incorporated to establish a contextual connection between the information source and task. The redundant components within an image will be removed based on different task objectives during the process of image transmission. The efficacy of the proposed approach is evaluated across three channel models varying in complexity. Simulation results underscore the system's capacity to accommodate fluctuations in SNR and its role in facilitating the optimal execution of images communication tasks.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3521948", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4405755616", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Game-Guided Matching Theory-Based Resource Allocation for Secure Semantic Communications", "authors": ["Xingyu Yang", "Helin Yang", "Yifu Jiang", "Arokiaswami Alphones", "Liang Xiao"], "year": 2024, "publication_date": "2024-12-27", "doi": "10.1109/tvt.2024.3523524", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "5", "pages": "8357-8362", "abstract": "Semantic communication (SemCom) has become one of the most promising techniques in breaking the performance bottleneck for sixth-generation wireless networks, but the SemCom performance is easily degraded under malicious jamming and eavesdropping attacks over open wireless links. Therefore, this paper designs a reliable and secure SemCom approach against a hybrid attacker based on resource allocation, with the objective to jointly optimize channel selection and the number of transmitted semantic symbols to maximize secrecy semantic transmission rate (SS-R) under different quality of service requirements. We adopt a hierarchical framework based on Stackelberg game to model the interactions between legitimate users and the hybrid attacker. Furthermore, we model the optimization problem as a many-to-one matching problem with externalities, and propose two swap-based resource allocation algorithms, aiming to maximize the overall SS-R and meet fairness awareness. The two proposed algorithms are able to guide the iteration properly based on the dynamic of attack behaviors, which improves the secure resource allocation efficiency. Simulation results show that the proposed approaches outperform the baselines and benchmarks under different scenarios.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3523524", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4405846319", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://zaguan.unizar.es/record/150523/files/texto_completo.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication", "semantic transmission"]}
{"title": "Joint Active and Passive Beamforming for RIS-Aided Semantic Communication", "authors": ["Yinuo Huang", "Chang Cai", "Xiaojun Yuan", "Ying–Jun Angela Zhang"], "year": 2024, "publication_date": "2024-07-30", "doi": "10.1109/tvt.2024.3435754", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "12", "pages": "19815-19820", "abstract": "Reconfigurableintelligent surface (RIS) is a promising technology to enhance the performance of the semantic communication system. Despite its great potential, the performance gain of integrating RIS into semantic communications is not fully exploited. This is mainly because the design objective of the physical layer does not match with that of the learning-based codecs. In this paper, we introduce a unified design framework that aligns the learning and communication objectives based on the Infomax principle. In this framework, the learning (feature encoding and classification) and communication (joint active and passive beamforming) modules are separately designed to reduce complexity. We further develop a block coordinate descent (BCD) algorithm to solve the joint beamforming problem. We conducted the classification task on the CIFAR-10 dataset. Simulation results demonstrate that the proposed method achieves superior classification accuracy compared to the benchmarks.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3435754", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4401113937", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Joint Source-Channel Coding for Image Super-Resolution Tasks in Semantic Communications", "authors": ["Zhen Huang", "Yunjian Jia", "Yulu Zhang", "Xinyue Liu", "Huicong Shen", "Wanli Wen"], "year": 2024, "publication_date": "2024-08-13", "doi": "10.1109/tvt.2024.3442204", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "12", "pages": "19034-19039", "abstract": "Image super-resolution (SR) technology is crucial for applications such as telemedicine. Performing image SR tasks using semantic communication allows the received information to be directly applied without the need for complex operations at the receiver. However, research still grapples with the challenges of constructing a joint source-channel coding (JSCC) method that effectively extracts semantic information from LR images for reconstructing HR images, as well as reducing the unfavorable impact of the wireless channel. In this paper, we propose a deep semantic information extraction (DSIE) module for the image SR task in semantic communications. This module ingeniously integrates the advantages of convolutional neural network (CNN) and Transformer architectures and incorporates signal-to-noise ratio (SNR) to dynamically adjust the output via the channel attention mechanism. Building on DSIE, we custom-design the JSCC scheme for image SR tasks in semantic communications. Simulation results confirm the effectiveness of our proposed DSIE and show that the proposed JSCC scheme surpasses the existing DNN-based JSCC schemes and the traditional separated-source channel coding schemes.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3442204", "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", "vehicular semantic communication", "V2X semantic communication", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4401539977", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "joint source-channel coding", "JSCC", "semantic communication", "Semantic Communication"]}
{"title": "Knowledge Discrepancy Oriented Privacy Preserving for Semantic Communication", "authors": ["Shiqi Cheng", "Xuefei Zhang", "Yao Sun", "Qimei Cui", "Xiaofeng Tao"], "year": 2024, "publication_date": "2024-03-25", "doi": "10.1109/tvt.2024.3381222", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "8", "pages": "11637-11646", "abstract": "Semantic communication is an emerging paradigm relying on deep neural network alongside knowledge bases to enable correct delivery of desired information meaning. Most existing semantic communication systems assume that communication nodes share the same public knowledge base, while ignoring the agnostic and diversity of private knowledge that may lead to a knowledge discrepancy. Consequently, the receiver can infer sensitive information that the sender does not want to disclose. To address this privacy risk, we provide a knowledge discrepancy oriented privacy preserving method (KDPP) for semantic communication. KDPP aims to narrow down the discrepancy between prior knowledge and receiver's knowledge before privacy preserving. Specifically, we propose knowledge mapping to solve the agnostic problem, matching the unknown knowledge to the known prior knowledge. Moreover, we introduce disambiguation to address the diversity issue, capturing diverse interpretations of ambiguous concepts. Our simulations show that KDPP performs better than existing methods in terms of the inference accuracy and the privacy preserving effect. This demonstrates the validity of private knowledge inference as an effective method for privacy preserving against knowledge discrepancy.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3381222", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4393171242", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Maximal Coding Rate Reduction: A Unified Approach for Task-Oriented RIS and Communication", "authors": ["Jie Zhou", "Shuaishuai Guo", "Jia Ye", "Peng Zhang", "Ce Zheng"], "year": 2024, "publication_date": "2024-12-30", "doi": "10.1109/tvt.2024.3523674", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "5", "pages": "8363-8368", "abstract": "Reconfigurable intelligent surfaces (RIS) have shown promise in enhancing wireless communication by intelligently adjusting signal propagation. This study explores a RIS-assisted multi-device communication system focusing on task-oriented communication in edge server inference tasks. Unlike traditional RIS designs that optimize channels without task relevance, we propose a unified objective, maximal coding rate reduction (MCR$^{2}$), to optimize RIS, feature encoder, and channel precoder. We introduce alternative block gradient projection (ABGP) and reflecting gradient project (RGP) algorithms to design precoder and RIS. Simulation results demonstrate that our approach significantly outperforms conventional methods in extreme indoor environments with low signal-to-noise ratio (SNR). It achieves comparable performance to general RIS designs with fewer elements, ensuring task-specific enhancements. We further extend our method to active RIS, addressing the double path fading issue in passive RIS and improving performance under equal power budgets.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3523674", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4405907550", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["task-oriented communication"]}
{"title": "Novel Double Protograph LDPC Codes for Joint Source-Channel Coding Systems", "authors": ["Jia Zhan", "Wai-Man Tam", "Francis C. M. Lau"], "year": 2024, "publication_date": "2024-11-28", "doi": "10.1109/tvt.2024.3507919", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "4", "pages": "5291-5306", "abstract": "In this paper, we first propose a novel joint source-channel block code (JSC-BC) in which two protograph-based low-density parity-check (P-LDPC) block codes are connected not only by a source-variable-channel-check (SVCC) linking matrix, but also by a source-check-channel-variable (SCCV) linking matrix that consists of a zero matrix and a lower or upper triangular (base) matrix with “1”s on its diagonal. Also, we simplify the traditional joint protograph extrinsic information transfer (JP-EXIT) algorithm and propose an “untransmitted protograph-based EXIT (UP-EXIT) algorithm” for calculating the source threshold of a JSC-BC. The proposed UP-EXIT algorithm is more efficient because a smaller protograph consisting of only the untransmitted VNs (i.e., the source VNs and the punctured channel VNs) and their connected check nodes need to be considered. By using the UP-EXIT algorithm, we search for candidate codes with high source thresholds. Then, we select those among the candidate codes also with low channel thresholds. Theoretical and simulation results show that the newly constructed codes outperform state-of-the-art double P-LDPC (DP-LDPC) block codes. Furthermore, we spatially couple the joint source-channel block code and obtain a spatially coupled joint source-channel code (SC-JSCC). Theoretical analyses and simulation results show that even with a smaller window size and lower decoding complexity, the SC-JSCC with the spatially coupled structure for each sub-block (source protomatrix, channel protomatrix, SCCV linking matrix, and SVCC linking matrix) can achieve better error performance than existing spatially-coupled DP-LDPC codes.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3507919", "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", "joint source channel coding image transmission", "joint source channel coding video transmission"], "openalex_id": "https://openalex.org/W4404810623", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "JSCC"]}
{"title": "Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication", "authors": ["Jinhyuk Choi", "Jihong Park", "Seung‐Woo Ko", "Jinho Choi", "Mehdi Bennis", "Seong‐Lyun Kim"], "year": 2024, "publication_date": "2024-06-17", "doi": "10.1109/tvt.2024.3410380", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "10", "pages": "15815-15819", "abstract": "Recent studies on semantic communication commonly rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC). Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. On the flip side, each neural transceiver is inherently biased towards specific source data and channels, making different transceivers difficult to understand intended semantics, particularly upon their initial encounter. To align semantics over multiple neural transceivers, we propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques. In this method, referred to as SL with layer freezing (SLF), each encoder downloads a misaligned decoder, and locally fine-tunes a fraction of these encoder-decoder NN layers. By adjusting this fraction, SLF controls computing and communication costs. Simulation results confirm the effectiveness of SLF in aligning semantics under different source data and channel dissimilarities, in terms of classification accuracy, reconstruction errors, and recovery time for comprehending intended semantics from misalignment.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3410380", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4399728407", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://doi.org/10.1109/tvt.2024.3410380", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["DeepJSCC", "semantic communication", "Semantic Communication"]}
{"title": "SNN-SC: A Spiking Semantic Communication Framework for Collaborative Intelligence", "authors": ["Mengyang Wang", "Jiahui Li", "Mengyao Ma", "Xiaopeng Fan"], "year": 2024, "publication_date": "2024-11-25", "doi": "10.1109/tvt.2024.3506119", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "4", "pages": "5883-5896", "abstract": "Collaborative Intelligence (CI) has emerged as a promising framework for deploying Artificial Intelligence (AI) models on resource-constrained edge devices. In CI, the AI model is partitioned between the edge device and the cloud, with intermediate features transmitted from the edge sub-model to the cloud sub-model to complete the inference task. However, reducing feature transmission overhead while maintaining task performance remains a challenge, particularly in the case of noisy wireless channels. In this paper, we propose a Spiking Neural Network (SNN)-based Semantic Communication (SC) model, SNN-SC, which extracts compact semantic information from features and transmits it through digital binary channels. Compared to the Deep Neural Network (DNN)-based SC model, whose output is floating-point, the binary output of SNN makes SNN-SC directly applicable to digital binary channels without the need for extra quantization. Moreover, we introduce a novel spiking neuron called IHF to enhance the reconstruction capability of the SNN-SC decoder. Finally, we enhance the performance of SNN-SC by maximizing the entropy of semantic information. SNN-SC achieves a higher compression ratio and overcomes the ‘cliff effect’ compared to the traditional separate source and channel coding method. In addition, SNN-SC has lower computational complexity than the DNN-based SC model and maintains higher task performance under poor channel conditions.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3506119", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4404688700", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2210.06836", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "STAR-RIS-Assisted Privacy Protection in Semantic Communication System", "authors": ["Yiru Wang", "Wanting Yang", "Pengxin Guan", "Yuping Zhao", "Zehui Xiong"], "year": 2024, "publication_date": "2024-04-01", "doi": "10.1109/tvt.2024.3383824", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "9", "pages": "13915-13920", "abstract": "Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to convert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3383824", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4393372927", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2306.12675", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "Swin Transformer-Based Dynamic Semantic Communication for Multi-User With Different Computing Capacity", "authors": ["Loc X. Nguyen", "Ye Lin Tun", "Yan Kyaw Tun", "Minh N. H. Nguyen", "Chaoning Zhang", "Zhu Han", "Choong Seon Hong"], "year": 2024, "publication_date": "2024-02-06", "doi": "10.1109/tvt.2024.3362328", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "6", "pages": "8957-8972", "abstract": "Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3362328", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4391559592", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2307.03402", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Tasks-Oriented Channel Optimization and Resource Allocation in Vehicular Networks: A Hierarchical Reinforcement Learning Based Approach", "authors": ["Runtao Lin", "Caili Guo", "Biling Zhang", "Jiujiu Chen", "Hanyang Li"], "year": 2024, "publication_date": "2024-12-27", "doi": "10.1109/tvt.2024.3523499", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "5", "pages": "7624-7636", "abstract": "The number of devices, data volume, and pictures and videos in the vehicular networks have all increased significantly as a result of the continued development of 6G technology, putting an enormous burden on the edge communication resources. To solve this problem, a good resource allocation scheme is necessary. Existing resource allocation algorithms often pursue accurate transmission and ignore time-varying channels. In order to further obtain real and reasonable resource allocation results, this paper introduces semantic communication. It then decomposes that problem to further establish a hierarchical framework. The problem is addressed via a task-oriented channel optimization and resource allocation scheme based on a hierarchical reinforcement learning (TCRS-HRL) algorithm. First, a channel optimization and resource allocation problem under an imperfect channel state is developed, and it is divided into two separate sub problems for optimization. Second, the resource allocation problem at the edge is addressed with the introduction of hierarchical reinforcement learning and the TCRS-HRL algorithm. The ideal channel state and resource allocation approach are found by constructing the state space, action space, and environmental feedback for each aspect individually. Finally, simulations have demonstrated the superiority and performance benefits of the proposed algorithm compared to current methods, showing that the implementation of TCRS-HRL can result in an accuracy improvement of roughly 8%.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3523499", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4405846296", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["semantic communication"]}
{"title": "Two-Way Semantic Communications Without Feedback", "authors": ["Kaiwen Yu", "Qi He", "Gang Wu"], "year": 2024, "publication_date": "2024-01-16", "doi": "10.1109/tvt.2024.3352666", "venue": "IEEE Transactions on Vehicular Technology", "volume": "73", "issue": "6", "pages": "9077-9082", "abstract": "Semantic communications can significantly improve transmission efficiency, especially in the low signal-to-noise (SNR) regime. However, two-way semantic communications still remain an unexplored topic, which is a critical issue in many machine communication scenarios. Simply extending existing semantic communication systems to the two-way situation requires bidirectional information feedback during the training process, resulting in significant communication overhead. To fill this gap, we investigate a two-way semantic communication (TW-SC) system, where the information feedback can be omitted by exploiting the weight reciprocity in the transceiver. Particularly, the channel simulator and semantic transceiver are implemented on both TWSC nodes and the channel distribution is modeled by a conditional generative adversarial network. Simulation results demonstrate that the proposed TW-SC system performs closing to the stateof-the-art one-way semantic communication systems but requiring no feedback between the transceiver in training process.", "publisher_url": "https://doi.org/10.1109/tvt.2024.3352666", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4390905864", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2306.08903", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Attention-Driven Interference Alignment for Multi-User Semantic Communications", "authors": ["Ki-Ho Lee", "Hyun-Ho Choi", "Jung-Ryun Lee"], "year": 2025, "publication_date": "2025-12-02", "doi": "10.1109/tvt.2025.3639430", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "11843-11848", "abstract": "This paper presents an attention-driven interference alignment (IA) scheme for multi-user semantic communications that operates in the semantic embedding domain, unlike conventional IA in the baseband domain. To realize this, user-specific masking and attention-driven decoding are employed over a shared embedding space, which is mathematically modeled to characterize attention-based IA in the embedding domain and to enable implicit user separation without explicit orthogonalization. Built on a conventional cross-entropy (CE) loss, the proposed orthogonalization regularization further reinforces interference alignment by aligning user-specific semantic subspaces and suppressing inter-user interference. Experiments under Rayleigh fading channels show that the proposed approach outperforms both a resource-partitioned baseline with separate embeddings and a CE-only loss model without orthogonalization regularization, showing the effectiveness of an attention-driven IA solution for multi-user semantic communication.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3639430", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4416922629", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Bandwidth Management in Semantic Communications: A Tradeoff Between Data Sensing and Transmission", "authors": ["Shuheng Hua", "Yao Sun", "Kairong Ma", "Lei Feng", "Mingkai Chen", "Zhaohui H. Yang", "Muhammad Ali Imran"], "year": 2025, "publication_date": "2025-07-18", "doi": "10.1109/tvt.2025.3590642", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "1", "pages": "617-630", "abstract": "Semantic communication (SemCom) is a novel paradigm that exploits sophisticated deep learning tools to distil semantic features from source data at transmitter and recover the meaning of the source data based on these semantic features at the receiver. To train proficient semantic coding models with minimum semantic ambiguity, it is of paramount importance to sense abundant data for both model training and background knowledge construction. However, if interminable sensing is performed for getting highly capable SemCom coding models, a large amount of bandwidth resources should be occupied, which may reduce semantic spectrum efficiency (SSE). In this paper, we investigate how to achieve an efficient tradeoff between data transmission and data sensing in SemCom. Specifically, we first formulate an optimization problem to jointly allocate bandwidth for sensing and data transmission with the objective of maximizing SSE, while subject to resource budgets and service quality requirements. To solve this problem, we start with a simplified resource allocation policy where bandwidth is equally allocated to all users. Inspired by the solution to equal allocation, we then derive the bandwidth allocation policy under a generic non-equal allocation scenario, based on the projected gradient method considering the variables sensing bandwidths and data transmission bandwidths are interdependent. In addition, we extend to the scenario of multi encoder models, and divide the optimization into two tractable subproblems. Furthermore, numerical simulations verify the effectiveness and superior performance of our proposed algorithm on the SSE, compared with several baselines.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3590642", "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", "product quantization semantic communication", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "image transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4412871907", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic coding", "Semantic communication", "Semantic Communication"]}
{"title": "Coarse-to-Fine Semantic Communication Systems for Text Transmission", "authors": ["Mengli Tao", "Jiancun Fan", "Jie Luo", "Huiqiang Xie"], "year": 2025, "publication_date": "2025-04-03", "doi": "10.1109/tvt.2025.3557400", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "8", "pages": "13267-13271", "abstract": "Achieving more powerful semantic representations and semantic understanding is one of the key problems in improving the performance of semantic communication systems. This work focuses on enhancing the semantic understanding of the text data to improve the effectiveness of semantic exchange. We propose a novel semantic communication system for text transmission, in which the semantic understanding is enhanced by coarse-to-fine processing. Especially, a dual attention mechanism is proposed to capture both the coarse and fine semantic information. Numerical experiments show the proposed system outperforms the benchmarks in terms of bilingual evaluation, sentence similarity, and robustness under various channel conditions.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3557400", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4409129501", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Cross-Layer Security for Semantic Communications: Metrics and Optimization", "authors": ["Lingyi Wang", "Wei Wu", "Fuhui Zhou", "Zhijin Qin", "Qihui Wu"], "year": 2025, "publication_date": "2025-03-19", "doi": "10.1109/tvt.2025.3552777", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "8", "pages": "13179-13183", "abstract": "Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to fill this gap, a unified semantic security metric, the cross-layer semantic secure rate (CLSSR), is defined to estimate cross-layer security requirements at both the physical layer and the application layer. Then, we formulate the maximization problem of the CLSSR with the mixed integer nonlinear programming (MINLP). We propose a hierarchical AI-native semantic secure communication network with a reinforcement learning (RL)-based semantic resource allocation scheme, aiming to ensure the cross-layer semantic security (CL-SS). Finally, we prove the convergence of our proposed intelligent resource allocation, and the simulation results demonstrate that our proposed CLSS method outperforms the traditional physical layer semantic security (PL-SS) method in terms of both task reliability and CLSSR.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3552777", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4408655639", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Cross-Modal Domain-Adaptive Semantic Communication", "authors": ["Xuefei Zhang", "Xuqin Tang", "Shiqi Cheng", "Yao Sun", "Qimei Cui", "Xiaofeng Tao"], "year": 2025, "publication_date": "2025-12-19", "doi": "10.1109/tvt.2025.3646280", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "10722-10733", "abstract": "Semantic communication leverages the powerful representation and inference capabilities of deep neural networks (DNNs) to achieve efficient data compression and reconstruction, significantly improving communication efficiency and robustness. However, due to the inherently poor generalization ability of DNNs, the performance degradation appears once the data to be transmitted does not follow the independent identical distribution (i.i.d.) with the training dataset. Especially for the multimodal data, different modalities exhibit significant disparities in data structure and semantic representation, which leads to more outstanding non-i.i.d. problem. To tackle this challenge, we leverage strong inter-modal correlations, where a modality that is less affected by distribution shifts is used to guide the other modality in distribution alignment, ensuring that the transmitted data maintains a similar distribution to the training dataset. On this basis, we propose Cross-modal Domain-Adaptive Semantic Communication (CDASC), which utilizes cross-modal guidance to adapt the feature distribution of transmitted data in real time. Taking audio-video transmission as an example, we demonstrate that CDASC offers significant advantages in both data reconstruction quality and task accuracy.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3646280", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4417508740", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "Decentralized Semantic Communication and Cooperative Tracking Control for a UAV Swarm Over Wireless MIMO Fading Channels", "authors": ["Minjie Tang", "Chenyuan Feng", "Tony Q. S. Quek"], "year": 2025, "publication_date": "2025-08-23", "doi": "10.1109/tvt.2025.3601731", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "3354-3359", "abstract": "Conventional communication strategies in UAV swarms often lead to excessive bandwidth consumption and energy overhead due to frequent exchange of control signals. Inspired by the semantic communication paradigm—which emphasizes transmitting only task-relevant information—we propose a cooperative semantic communication-control framework that selectively transmits the most informative control data. This approach significantly reduces communication burden and power consumption while maintaining accurate swarm coordination. Specifically, we consider a UAV swarm composed of one leader and multiple followers, interconnected through unreliable MIMO wireless channels. We first develop a dynamic model that captures both inter-UAV interactions and MIMO channel imperfections. Incorporating power costs, we formulate the joint communication and cooperative tracking control problem as a drift-plus-penalty optimization. A closed-form decentralized solution is then derived, adapting to tracking errors and local channel conditions. Using Lyapunov drift analysis, we establish sufficient conditions for swarm stability. Numerical simulations demonstrate that the proposed scheme substantially outperforms existing methods in both tracking accuracy and communication efficiency.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3601731", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4413472172", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2411.06136", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Delay-Aware Digital Twin Synchronization in Mobile Edge Networks With Semantic Communications", "authors": ["Bin Li", "Haichen Cai", "Lei Liu", "Zesong Fei"], "year": 2025, "publication_date": "2025-03-07", "doi": "10.1109/tvt.2025.3548844", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "7", "pages": "10974-10983", "abstract": "The synchronization of digital twins (DT) serves as the cornerstone for effective operation of the DT framework. However, the limitations of channel capacity can greatly affect the data transmission efficiency of wireless communication. Unlike traditional communication methods, semantic communication transmits the intended meanings of physical objects instead of raw data, effectively saving bandwidth resource and reducing DT synchronization latency. Hence, we are committed to integrating semantic communication into the DT synchronization framework within the mobile edge computing system, aiming to enhance the DT synchronization efficiency of user devices (UDs). Our goal is to minimize the average DT synchronization latency of all UDs by jointly optimizing the synchronization strategy, transmission power of UDs, and computational resource allocation for both UDs and base station. The formulated problem involves sequential decision-making across multiple coherent time slots. Furthermore, the mobility of UDs introduces uncertainties into the decision-making process. To solve this challenging optimization problem efficiently, we propose a soft actor-critic-based deep reinforcement learning algorithm to optimize synchronization strategy and resource allocation. Numerical results demonstrate that our proposed algorithm can reduce synchronization latency by up to 13.2% and improve synchronization efficiency compared to other benchmark schemes.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3548844", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "feature transmission edge inference", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4408223328", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2503.04387", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Distributed Hierarchical Deep Reinforcement Learning for Semantic-Aware Resource Allocation", "authors": ["Kaiwen Yu", "Qi He", "Chuanhang Yu", "Xingyu Yang", "Gang Wu"], "year": 2025, "publication_date": "2025-05-19", "doi": "10.1109/tvt.2025.3571485", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "10", "pages": "16322-16334", "abstract": "Beyond the traditional quality of experience (QoE) optimization that focuses on bit transmission, taking into account semantic QoE can better optimize the network to improve user experience. This paper develops a novel distributed hierarchical deep reinforcement learning (DHDRL) framework with a two-layer control strategy in different timescales to optimize QoE in the downlink multi-cell network including the semantic rate and accuracy. Specifically, we decompose the optimization problem into two sub-problems, beamforming and power allocation as well as semantic resource allocation, resulting in an infinite-horizon discrete and a finite-horizon continuous-time Markov decision processes. Two-layer neural networks (NNs) are proposed to solve the sub-problems. At the network level, a distributed high-level NN is proposed to mitigate the inter-cell interference and improve the network performance on a large timescale, while at the link level, another distributed low-level NN to improve the semantic compression rate and meet various semantic accuracy and rate requirements on a small timescale. Numerical results show that DHDRL achieves at least a 19.07% performance gain over the single-layer method, maintains effectiveness in high-dimensional scenarios, and exhibits strong generalization across a variety of multi-objective optimization problems.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3571485", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4410492181", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["Semantic-Aware Resource"]}
{"title": "Generative Semantic Communication via Textual Prompts: Latency Performance Tradeoffs", "authors": ["Mengmeng Ren", "Li Qiao", "Long Xiang Yang", "Zhen Gao", "Jian Chen", "Mahdi Boloursaz Mashhadi", "Pei Xiao", "Rahim Tafazolli", "Mehdi Bennis"], "year": 2025, "publication_date": "2025-05-02", "doi": "10.1109/tvt.2025.3566488", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "9", "pages": "14843-14848", "abstract": "This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed framework optimizes the use of M/VLMs on the wireless edge/device to generate high-fidelity textual prompts through visual captioning/question answering, which are then transmitted over a wireless channel for SemCom. Specifically, we develop a multi-user Gen SemCom framework using pre-trained M/VLMs, and formulate a joint optimization problem of prompt generation offloading, communication and computation resource allocation to minimize the latency and maximize the resulting semantic quality. Due to the non-convex nature of the problem with highly coupled discrete and continuous variables, we decompose it as a two-level problem and propose a low-complexity swap/leaving/joining (SLJ)-based matching algorithm. Simulation results demonstrate significant performance improvements over the conventional semantic-unaware/ non-collaborative generation offloading benchmarks.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3566488", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4410028549", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2409.09715", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Generative Video Semantic Communication via Multimodal Semantic Fusion With Large Model", "authors": ["Hang Yin", "Li Qiao", "Yu Ma", "Shuo Sun", "Kan Li", "Zhen Gao", "Dusit Tao Niyato"], "year": 2025, "publication_date": "2025-08-04", "doi": "10.1109/tvt.2025.3595688", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "1", "pages": "1701-1706", "abstract": "Despite significant advancements in traditional syntactic communications based on Shannon's theory, these methods struggle to meet the requirements of 6 G immersive communications, especially under challenging transmission conditions. With the development of generative artificial intelligence (GenAI), progress has been made in reconstructing videos using high-level semantic information. In this paper, we propose a scalable generative video semantic communication framework that extracts and transmits semantic information to achieve high-quality video reconstruction. Specifically, at the transmitter, description and other condition signals (e.g., first frame, sketches, etc.) are extracted from the source video, functioning as text and structural semantics, respectively. At the receiver, the diffusion-based GenAI large models are utilized to fuse the semantics of the multiple modalities for reconstructing the video. Simulation results demonstrate that, at an ultra-low channel bandwidth ratio (CBR), our scheme effectively captures semantic information to reconstruct videos aligned with human perception under different signal-to-noise ratios. Notably, the proposedFirst Frame+Desc.scheme consistently achieves CLIP score exceeding 0.92 at CBR = 0.0031 for SNR$>$0 dB. This demonstrates its robust performance even under low SNR conditions.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3595688", "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 video transmission", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "video transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4412939921", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2502.13838", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Heterogeneous Semantic and Bit Communications in UAV Network: A Semi-NOMA Design", "authors": ["Sicheng Wang", "Tiankui Zhang", "Xiaoxia Xu", "Yuanwei Liu"], "year": 2025, "publication_date": "2025-11-26", "doi": "10.1109/tvt.2025.3637603", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "11265-11278", "abstract": "This article proposes a novel heterogeneous semantic and bit communications framework in an uncrewed aerial vehicle (UAV) network, which enables uplink image transmissions from terrestrial user equipments (UEs) to an airborne mobile base station (BS). Semi-non-orthogonal multiple access (NOMA) is exploited to serve two categories of UEs, namely bit user equipments (BUEs) and semantic user equipments (SUEs). BUEs transmit critical data with the aim of enhancing conventional bit rate metrics. In contrast, SUEs transmit semantic representations of images compressed by deep joint source-channel coding (DeepJSCC), thus pursuing higher semantic rates. A unified metric termed hybrid semantic-and-bit rate is introduced to evaluate the system performance, which is maximized by jointly optimizing the UAV trajectory, user association, and resource allocation. A proximal policy optimization (PPO) based trajectory optimization and resource allocation (TORA) algorithm is proposed to tackle this NP-hard problem in a time-varying environment. The proposed algorithm enables the UAV to interact with the dynamic environment to explore efficient trajectory planning and resource allocation. Simulation results demonstrate that: 1) the proposed TORA algorithm achieves a higher hybrid semantic-and-bit rate compared to baseline algorithms under various compression rates (CRs) with fast convergence; and 2) the semi-NOMA transmission scheme achieves superior performance compared to NOMA and time division multiple access (TDMA) transmission schemes under various CRs, even when the number of BUE-SUE user pairs increases.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3637603", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4416707573", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["deep joint source-channel", "DeepJSCC"]}
{"title": "Integrated Sensing, Computing and Semantic Communication for Vehicular Networks", "authors": ["Yinchao Yang", "Zhaohui Yang", "Chongwen Huang", "Wei Xu", "Zhaoyang Zhang", "Dusit Tao Niyato", "Mohammad Shikh‐Bahaei"], "year": 2025, "publication_date": "2025-06-12", "doi": "10.1109/tvt.2025.3575243", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "11", "pages": "18307-18312", "abstract": "This paper introduces a novel framework for integrated sensing, computing, and semantic communication (ISCSC) within vehicular networks comprising a roadside unit (RSU) and multiple autonomous vehicles. Both the RSU and the vehicles are equipped with local knowledge bases to facilitate semantic communication. The framework incorporates a secure communication design to ensure that messages intended for specific vehicles are protected against interception. Within this model, an extended Kalman filter (EKF) is employed by the RSU to track all vehicles accurately. We formulate a joint optimisation problem that balances maximising the probabilistic-constrained semantic secrecy rate for each vehicle while minimising the sum of the posterior Cramér-Rao bound (PCRB), constrained by the RSU's computing capabilities. This non-convex optimisation problem is addressed using Bernstein-type inequality (BTI) and alternating optimisation (AO) techniques. Simulation results validate the effectiveness of the proposed framework, highlighting its advantages in reliable sensing, high data throughput, and secure communication.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3575243", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4411232656", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2506.00522", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Joint Design of Sensing, Communication, and Computation for Multi-AAV-Enabled Over-the-Air Federated Learning", "authors": ["Yang Fu", "Peng Da Qin", "Guoming Tang", "Xiongwen Zhao"], "year": 2025, "publication_date": "2025-04-09", "doi": "10.1109/tvt.2025.3559147", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "9", "pages": "13909-13924", "abstract": "AI integrated Communication is envisaged as one of the novel usage scenarios in IMT-2030, which calls for embedding AI into network edge to offer ubiquitous intelligent services. Meanwhile, federated learning (FL) is an appealing technology for edge intelligence due to its advantages on distributed training and privacy preserving. However, FL suffers from several limitations when being executed in wireless networks, such as high communication overhead, low deployment flexibility, and the lack of analytical performance characterization. To overcome such challenges, we propose a multi-unmanned aerial vehicle (UAV)-enabled over-the-air federated learning system, where devices sense data samples for local training, and exchange model-updates with UAVs employing analog transmission scheme. Under this scheme, we first conduct convergence analysis to characterize the influence of sensing sample size, downlink broadcasting distortions, and uplink aggregation errors on the FL performance with a closed-form expression. Subsequently, a joint sensing, communication, and computation (JSCC) design problem is established to boost the FL performance among different UAVs, under the constraints of training completion time and network resource budget. We then tackle the origin intractable problem by alternatively optimizing downlink power control, uplink transceiver design, UAV deployment, and sensing batch size. Experimental evaluations demonstrate the significant performance improvement of our JSCC design compared to conventional approaches, and the effectiveness of adaptively allocating network resources across different FL rounds in suppressing the model updating errors.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3559147", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4409310614", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["JSCC"]}
{"title": "Joint Knowledge and Power Management for Secure Semantic Communication Networks", "authors": ["Xuesong Liu", "Yansong Liu", "Haoyu Tang", "Fangzhou Zhao", "Le Xia", "Yao Sun"], "year": 2025, "publication_date": "2025-11-25", "doi": "10.1109/tvt.2025.3636880", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "10171-10184", "abstract": "Recently, semantic communication (SemCom) has shown its great superiorities in resource savings and information exchanges. However, while its unique background knowledge guarantees accurate semantic reasoning and recovery, semantic information security-related concerns are introduced at the same time. Since the potential eavesdroppers may have the same background knowledge to accurately decrypt the private semantic information transmitted between legal SemCom users, this makes the knowledge management in SemCom networks rather challenging in joint consideration with the power control. To this end, this paper focuses on jointly addressing three core issues of power allocation, knowledge base caching (KBC), and device-to-device (D2D) user pairing (DUP) in secure SemCom networks. We first develop a novel performance metric, namely semantic secrecy throughput (SST), to quantify the information security level that can be achieved at each pair of D2D SemCom users. Next, an SST maximization problem is formulated subject to secure SemCom-related delay and reliability constraints. Afterward, we propose a security-aware resource management solution using the Lagrange primal-dual method and a two-stage method. Simulation results demonstrate our proposed solution nearly doubles the SST performance and realizes less than half of the queuing delay performance compared to different benchmarks.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3636880", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4416650084", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Learning to Decode Double Polar Codes for Joint Source-Channel Coding", "authors": ["Jian Shu Gao", "Xin Song", "Xiaojun Zhang", "Yanfei Dong", "Kai Niu", "Jincheng Dai"], "year": 2025, "publication_date": "2025-10-08", "doi": "10.1109/tvt.2025.3619411", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "4", "pages": "6013-6025", "abstract": "In this paper, a neural belief propagation decoder of double polar codes for joint source-channel coding (JSCC) is proposed. In the decoder, a residual structure is designed to establish information interaction mechanisms at different sizes between the neural sub-decoders and within them. To compensate for the information loss caused by source compression, a charger layer is introduced. In addition, we designed an iterative training scheme and parameter-sharing mechanism to constrain the complexity of the decoder. The multi-loss is extended to JSCC for training the decoder. It constrains the distribution of latent variables, thereby guiding the ResBP to optimize in the expected direction and accelerating training convergence. Experimental results show that the proposed neural decoder outperforms the existing BP-based decoders of double polar codes.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3619411", "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", "joint source channel coding image transmission", "joint source channel coding video transmission"], "openalex_id": "https://openalex.org/W4414956329", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "joint source-channel coding", "JSCC"]}
{"title": "Learning-Based Task-Centric Multi-User Semantic Communication Solution for Vehicle Networks", "authors": ["Yifan Yuan", "Jingxuan Zhang", "Xiaodong Xu", "Bizhu Wang", "Shujun Han", "Mengying Sun", "Ping Zhang"], "year": 2025, "publication_date": "2025-02-14", "doi": "10.1109/tvt.2025.3541019", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "6", "pages": "9328-9342", "abstract": "With the application of 5G in in-vehicle network scenarios, the inevitable crisis of scarcity of communication resources is deepening. At the same time, considering the high demands for communication delay in vehicular network scenarios, it is essential to meet the requirements for reliable information transmission in high-speed mobility environments. To address these issues, we propose the Task-Centric Multi-User Semantic Communication (TCMSC) solution, designed to meet the energy consumption and transmission time delay requirements in line with the new paradigm of semantic communication. Our solution introduces a task-centric semantic processing model aimed at improving Semantic Spectral Efficiency (S-SE). The TCMSC solution is tailored for multi-service vehicle scenarios, optimizing power consumption and enhancing reliability, making it well-suited for challenging environments. Moreover, we propose a novel method using Stochastic Network Calculus (SNC) to accurately model semantic task delay and calculate the upper bound of Vehicle-to-Infrastructure (V2I) delay-bound violation probability. However, to tackle the increased optimization complexity from SNC while simultaneously enhancing feature extraction, we propose the Transformer Advantage Actor Critic (TR-A2C) algorithm. This algorithm leverages the Transformer to capture dynamic vehicle parameters across scenarios, accelerating the TCMSC solution. Experimental results demonstrate that, compared to traditional single-service vehicle dispatch, TCMSC improves delay violation probability by${2.88\\%}$and reduces power consumption by${31.1\\%}$, all while effectively enhancing S-SE in complex traffic environments.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3541019", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4407574982", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Lightweight Digital Semantic Communication Based on DeepReceiver", "authors": ["Yunqi Feng", "Jianping Yu", "Weidang Lu", "Nan Zhao", "Arumugam Nallanathan"], "year": 2025, "publication_date": "2025-07-31", "doi": "10.1109/tvt.2025.3594122", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "1", "pages": "1666-1670", "abstract": "Semantic communication (SemCom) has garnered significant interest due to its potential to minimize data transmission volume without compromising communication effectiveness. However, existing research mainly focus on analog SemCom that directly maps the semantic features extracted from the source data into analog symbols, which restricts the applicability of SemCom in real-world scenarios. In this correspondence, we propose a novel digital SemCom with lightweight semantic vector quantization auto-encoder/decoder (L-VQAE) to align with the digital communication protocols. Moreover, considering the effect of channel noise, a deep learning (DL) based receiver (DeepReceiver) module is further introduced for symbol recovery. Simulation results demonstrate a significant enhancement in peak signal-to-noise ratio (PSNR) performance for the proposed system compared to the baselines.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3594122", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4412795783", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models", "authors": ["Chuanhong Liu", "Caili Guo", "Yang Yang", "Mingzhe Chen", "Tony Q. S. Quek"], "year": 2025, "publication_date": "2025-05-05", "doi": "10.1109/tvt.2025.3563635", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "9", "pages": "14823-14827", "abstract": "Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3563635", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4410087546", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2506.13243", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Mixture of Experts-Based Semantic Communication for Secure Artificial Intelligence of Things", "authors": ["Jiayi He", "Xiaofeng Luo", "Jiawen Kang", "Zehui Xiong", "Xiaohuan Li", "Ci Chen", "Dusit Tao Niyato"], "year": 2025, "publication_date": "2025-08-14", "doi": "10.1109/tvt.2025.3599157", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "3324-3329", "abstract": "Deep learning-based semantic communication systems have become prevalent in efficiently transmitting information in Artificial Intelligence of Things (AIoT), e.g., Intelligent Transportation Systems (ITS). However, their deployment over wireless networks introduces significant security vulnerabilities, including semantic noise attacks, physical layer adversarial attacks, and eavesdropping attacks. These threats may occur concurrently in practical scenarios, while existing defense mechanisms are typically designed to counter a single type of attack and struggle to address multiple heterogeneous threats. To tackle this, this paper introduces a Mixture of Experts-based Semantic Communication (MESC) system, which adaptively selects suitable security experts based on the security requirements of AIoT nodes. Numerical results indicate that the MESC system is capable of integrating specialized defense solutions to effectively mitigate multiple attacks with little loss of communication accuracy. Furthermore, the scalability of MESC substantially reduces ongoing costs by allowing the addition of new security experts with minimal training expenses.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3599157", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4413465579", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Multimodal Semantic Communications Empowered Lane-Change Planning for Autonomous Driving", "authors": ["Long Zhang", "Tao Song", "Lixin Li", "Lingqiang Chen", "Dusit Tao Niyato", "Zhu Han"], "year": 2025, "publication_date": "2025-08-19", "doi": "10.1109/tvt.2025.3599852", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "3330-3335", "abstract": "Integrating multimodal semantic communications with lane-change (LC) planning has been considered as a promising framework, enabling the vehicle-road collaborative autonomous driving with higher degree of efficiency and flexibility. However, it is crucial to dynamically adjust resource allocation based on multimodal semantic representations for adapting to the dynamic and complex traffic situations, which requires the co-design of motion planning and resource allocation. In this paper, we target on a joint optimization problem of the LC planning as well as the bandwidth and transmit power allocation at the modality transmitters of automated vehicles to minimize the overall latency of LC task completion. To tackle the highdimensional, dynamic, and uncertain challenges of this problem, a deep reinforcement learning-based co-design scheme which employs Dueling Double Deep Q-Network (D3QN) algorithm is proposed. Simulation results illustrate the superiority of our proposed scheme, achieving the overall latency reduction by at least 4.13 ms and the percentage of latency improvement by up to 98.21 over the benchmarks.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3599852", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4413344066", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Multimodal-Oriented Interactive Joint Source-Channel Coding for Lightweight Semantic Communication", "authors": ["Xupeng Niu", "Long Tan", "Jun Wu", "Weijie Yuan", "Tony Q. S. Quek"], "year": 2025, "publication_date": "2025-05-09", "doi": "10.1109/tvt.2025.3568851", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "10", "pages": "16516-16520", "abstract": "As a pivotal technology for sixth-generation (6G) mobile networks, semantic communication (SC) aims to enable low-energy, high-efficiency intelligent communication through joint source-channel coding (JSCC). However, current multimodal SC systems experience semantic divergence and conflicts due to imbalances in the representation capacity of individual encoders. Furthermore, high computational demands significantly elevate the deployment and operational costs of SC. In this letter, we propose a multimodal-oriented lightweight SC system based on an interactive JSCC scheme, promoting the evolution of 6G towards a multi-source collaboration paradigm. Additionally, a dynamic resource allocation method is introduced, which hierarchically adjusts computational costs to improve efficiency. Simulation results demonstrate that the proposed system reduces computational overhead by 60$\\%$compared to six benchmarks while maintaining high performance and robustness in multiple tasks.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3568851", "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", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "goal-oriented vehicular communication", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4410226807", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "joint source-channel coding", "JSCC", "semantic communication", "Semantic Communication"]}
{"title": "Online Learning for Wireless Image Semantic Transmission in Vehicular Networks", "authors": ["Xue Han", "Biqian Feng", "Yongpeng Wu", "Xiang‐Gen Xia", "Wenjun Zhang"], "year": 2025, "publication_date": "2025-10-20", "doi": "10.1109/tvt.2025.3623520", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "4", "pages": "6979-6984", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2025.3623520", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4415367463", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Transmission"]}
{"title": "PreNet Aided DF-JSCC: An Optimized Approach for Wireless Semantic Image Transmission Over Dynamic Radio Channels", "authors": ["Shuhan Yang", "Bin Shen", "Xiaoge Huang", "Qianbin Chen"], "year": 2025, "publication_date": "2025-09-10", "doi": "10.1109/tvt.2025.3608154", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "3", "pages": "4176-4191", "abstract": "Semantic communications provide a novelly simplified and intelligent paradigm for traditional wireless image transmissions. Although the existing wireless semantic image transmission (WSIT) schemes have made some progress in combating the “cliff effect” of performance degradation, they are still insufficient in terms of adaptivity, coding flexibility, and spectral efficiency for delivering diverse image contents in dynamic wireless channels. This paper proposes a deeply flexible joint source-channel coding (DF-JSCC) scheme and a transmission performance prediction network (PreNet) to ameliorate the overall WSIT system performance. Firstly, we incorporate a multi-channel feature fusion module (MC-FFM) into DF-JSCC to enhance the capability to capture detailed image features at different scales. Then, we design a feature enhancement module (FEM) to strengthen the representation of critical features by adaptively adjusting the channel weights of the image feature maps in various radio channel conditions. Finally, we introduce the PreNet, a model that achieves high-quality PSNR prediction for reconstructed images by modeling feature distribution from detail-rich feature maps and utilizing multidimensional feature fusion. Experimental results demonstrate that, compared with DeepJSCC-V and WITT schemes, DF-JSCC achieves PSNR improvements of at least 0.93 dB and 0.44 dB, respectively, while also reducing the resource consumption by more than 50% under the same transmission conditions, which underscores its superior performance in spectral efficiency. In addition, the PreNet has enhanced the prediction quality and robustness of the WSIT in complex environments by reducing 14% of the average prediction error.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3608154", "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", "vehicular semantic communication", "V2X semantic communication", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4414110806", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["DeepJSCC", "joint source-channel coding", "JSCC", "Semantic communication", "wireless image transmission"]}
{"title": "QoE-Aware Latency Optimization in Semantic Transmission Empowered Edge Assisted AR", "authors": ["Yang Li", "Yuan Wu", "Huanlai Xing", "Li Feng", "Tian Wang", "Weijia Jia"], "year": 2025, "publication_date": "2025-04-04", "doi": "10.1109/tvt.2025.3558337", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "9", "pages": "14432-14444", "abstract": "Augmented reality (AR) enhances human perception by overlaying digital information in the real environment and can be applied to various fields such as education, healthcare, and industry. To relieve the computing pressure of resourcelimited AR devices, edge assisted AR has attracted significant attention in recent years. It enables AR devices to offload the computation-intensive environment analysis tasks to the edge server and to download the point-based 3D virtual objects for rendering. Although this method can effectively reduce the processing latency, it may introduce a long transmission latency. To address this issue, this work proposes a semantic communication empowered edge assisted AR to reduce the quality of experience (QoE)-aware latency. To this end, the AR devices first utilize an image encoder to extract semantic features of source images. Due to the importance difference of extracted features and limited communication resources, the feature selection and bandwidth allocation are explored to improve transmission efficiency. In addition, when the edge server completes the generation of pointbased 3D virtual objects, it employs a transformer-based encoder to mask a proportion of points and learn a high-level representation of unmasked points. In this way, low-complexity semantic information of 3D objects is sent to the AR devices, which can reduce the transmission overhead of 3D objects. Moreover, the mask ratio of the point encoder is further optimized to balance the reconstructed quality and transmission latency of 3D objects. Therefore, this work aims to minimize the overall QoE-aware latency of edge assisted AR by jointly optimizing the semantic feature selection, mask ratio, and bandwidth allocation. A convexbased improved Harris Hawks optimization algorithm is designed to solve the formulated problem. Experimental results demonstrate that our semantic communication empowered edge assisted AR can effectively reduce the overall latency and outperform the other baselines", "publisher_url": "https://doi.org/10.1109/tvt.2025.3558337", "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", "vehicular semantic communication", "V2X semantic communication", "semantic-aware vehicular network", "image transmission vehicular network", "video transmission vehicular network", "feature transmission edge inference", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4409149020", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Transmission"]}
{"title": "Reliable Semantic Communication With QoE-Driven Resource Scheduling for UAV-Assisted MEC", "authors": ["Mengting Zheng", "Helin Yang", "Shuai Liu", "Kailong Lin", "Liang Xiao", "Zhu Han"], "year": 2025, "publication_date": "2025-03-10", "doi": "10.1109/tvt.2025.3542775", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "7", "pages": "11484-11489", "abstract": "Semantic communication is a great potential technology to improve the reliability and effectiveness of communication systems, but the limited computing capacity of users, the potential jamming attacks, and the inflexibility of fixed base stations pose significant challenges to communication efficiency. To overcome those challenges, this paper proposes a joint resource scheduling approach for unpiloted aerial vehicle (UAV)-assisted mobile edge computing (MEC) system based on reliable semantic communications against malicious jamming attacks. Specifically, we maximize the system's quality-of-experience (QoE) by jointly optimizing UAV trajectory, task offloading decision, and user association. Since the problem is non-convex and the jamming behavior is dynamic, an advanced softmax deep double deterministic policy gradients (SD3) based on the prioritized experience replay (PER) algorithm (called PER-SD3) is designed to realize an efficient communication strategy against jamming. Simulation results demonstrate that the proposed PER-SD3 approach can significantly improve the system delay and semantic communication performances compared with baseline approaches in the literature.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3542775", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4408281317", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Robust Semantic Communication via Adversarial Training", "authors": ["Kai Wei", "Renjie Xie", "Wei Xu", "Zhaohua Lu", "Huahua Xiao"], "year": 2025, "publication_date": "2025-07-03", "doi": "10.1109/tvt.2025.3585536", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "12", "pages": "19849-19853", "abstract": "The increasing demand for transmitting massive data in intelligent communication systems has garnered much attention on joint source-channel coding (JSCC) and semantic communication. Existing neural network (NN)-based semantic communication solutions predominantly focus on specific channel environments, which limits their adaptability to varying channel conditions. To address this challenge, we present a robust semantic communication approach that leverages adversarial (ADV) training to broaden its adaptability across diverse unseen channel conditions. While ADV training has been previously explored in semantic communications, its application in improving the robustness of JSCC in varying channel conditions remains underdeveloped. Specifically, our method enhances the robustness of semantic communication systems under varying channel conditions by incorporating ADV training, subjecting NNs to carefully crafted ADV perturbations that mimic diverse channel distortions. This enables the NN to perform more effectively when encountering changes in channel states. Simulation experiments demonstrate that integrating ADV training yields a substantial performance improvement of over 10% when channel states change, without increasing NN complexity. Notably, unlike existing methods that concentrate on specific channel models, our approach offers a more generalized solution that effectively adapts to a wide range of real-world channel conditions. This study contributes to the development of robust semantic communication systems empowered by ADV training, capable of adapting to diverse channel conditions in real-world scenarios.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3585536", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4411996728", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["joint source-channel coding", "JSCC", "semantic communication", "Semantic Communication"]}
{"title": "Robust Transmission of Punctured Text With Large Language Model-Based Recovery", "authors": ["Sojeong Park", "Hyeonho Noh", "Hyun Jong Yang"], "year": 2025, "publication_date": "2025-08-04", "doi": "10.1109/tvt.2025.3595593", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "1", "pages": "1737-1742", "abstract": "With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust performance regardless of dataset or tasks. In this correspondence, we propose a novel text transmission model that selects and transmits only a few characters and recovers the missing characters at the receiver using a large language model (LLM). Additionally, we propose a novel importance character extractor (ICE), which selects transmitted characters to enhance LLM recovery performance. Simulations demonstrate that the proposed filter selection by ICE outperforms random filter selection, which selects transmitted characters randomly. Moreover, the proposed model exhibits robust performance across different datasets and tasks and outperforms traditional communication under low signal-to-noise ratio conditions.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3595593", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4412939985", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2503.14831", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["semantic communication"]}
{"title": "Semantic Communication and Control Co-Design for Multi-Objective Distinct Systems", "authors": ["Abanoub M. Girgis", "Hyowoon Seo", "Mehdi Bennis"], "year": 2025, "publication_date": "2025-12-29", "doi": "10.1109/tvt.2025.3647657", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "11949-11954", "abstract": "This letter introduces a deep learning approach to predict the semantic control dynamics of different systems with different dynamics and control rules. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Moreover, the signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed semantic logical Koopman AE that reduces communication and computational costs without compromising control performance, showing a$91.65\\%$reduction in communication samples and significant performance gains in simulation.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3647657", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7117485828", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Semantic Communication-Based Aerial–Maritime Energy Trade-Off for Maritime Mobile Edge Computing Networks Under Jamming", "authors": ["Changyuan Xu", "Helin Yang", "Xingyu Yang", "Cheng Zhan"], "year": 2025, "publication_date": "2025-07-21", "doi": "10.1109/tvt.2025.3591032", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "1", "pages": "654-669", "abstract": "Unmanned aerial vehicles (UAVs)-enabled maritime mobile edge computing (MEC) effectively meets the increasing computational demands in maritime environments. However, the limited energy, computing resources, and bandwidth of wireless communication networks present significant challenges for timely computation and offloading efficiency. Moreover, the extensive line-of-sight (LoS) in maritime communication links makes MEC networks more vulnerable to malicious jamming than terrestrial networks. To this end, we investigate a semantic-aware aerial-maritime MEC network impacted by a malicious jammer, in which a UAV provides semantic communication (SemCom)-based task offloading services for maritime devices (MDs) with binary offloading. To reveal the energy trade-off between the MDs and UAV, our objective is to minimize their total weighted energy consumption by optimizing computing resources, UAV trajectory, and task offloading indicator, subject to task completion time constraints and semantic similarity constraints, in the presence of jamming. The formulated problem is a mixed binary variable strongly nonconvex problem, making it difficult to find the optimal solution. To tackle this problem, we propose a double-loop penalty-based alternative energy minimization (P-AEM) algorithm that achieves a high-quality suboptimal solution, therein exact penalty method (EPM) is employed to address binary constraints. To address the variables coupling and nonconvexity of the subproblems, we design a hybrid hierarchical difference of two convex (HD.C.) and logarithmic transformation optimization framework, followed by the successive convex approximation (SCA) technique to achieve a suboptimal solution. Simulation results demonstrate that the proposed scheme significantly reduces the total energy consumption compared with benchmark schemes and reveals the aerial-maritime energy trade-off.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3591032", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4412536810", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Semantic-Aware Beam Prediction Approach With Multimodal Fusion", "authors": ["Siting Lv", "Xiaohui Li", "Xingbo Chen", "Wenhui Gao", "Feixiang Liu", "Jiawen Liu", "Mingli Shi"], "year": 2025, "publication_date": "2025-10-01", "doi": "10.1109/tvt.2025.3616031", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "4", "pages": "6390-6403", "abstract": "Beam prediction is crucial to reduce the beam training overhead of large-scale antenna array systems. This paper proposes a semantic-aware beam prediction scheme based on multimodal fusion. Specifically, we develop a network architecture that integrates the semantic-aware network, encoder, multimodal fusion module, and decoder. The architecture takes environment images captured at the base station (BS) and coordinates text collected from GPS sensors equipped on targets as input, training the network to predict the optimal beam index. The semantic-aware network preprocesses the environment images captured at the BS, extracting key semantic images relevant to communication targets and discarding redundant environmental semantics to reduce data storage and computational complexity. In the multimodal fusion module, we employ a multimodal compact bilinear pooling (MCBP) method to reduce the complexity of outer product calculations, mapping the highdimensional results of the outer product to a lower-dimensional space for a more compact and efficient representation. Simulation results indicate that the beam prediction scheme proposed in this paper can achieve satisfactory accuracy without the need for pilot overhead and beam measurement and efficient prediction with low computational complexity.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3616031", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4414692582", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["semantic-aware network"]}
{"title": "Semantic-Based Resource Management Based on D2D Multicast Content Delivery: A Game-Theoretic Approach", "authors": ["Zhi Ji", "Xinrong Guan", "Jie Liu", "Xinxin Shen", "Meng Wang"], "year": 2025, "publication_date": "2025-01-14", "doi": "10.1109/tvt.2025.3529763", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "5", "pages": "7524-7538", "abstract": "Device-to-Device (D2D) multicast communication is an important component of wireless network design, bringing greater flexibility and efficiency to 5G and future network architectures. Utilizing semantic communication for D2D multicast is considered a promising approach. However, two challenges need to be addressed: user clustering and resource management based on semantic communication. For the first issue of user clustering, we introduce a semantic triple-based structure to extract semantic features of images and define user similarity for image delivery tasks based on semantic features and other indicators. Subsequently, improvements are made to the K-medoids algorithm to achieve efficient user clustering. For the other issue of resource management, a scaling compression factor is first designed to adjust the fidelity of image delivery for the semantic image communication process of semantic encoding and decoding. Then, we propose a mini-batch model training method by randomly selecting batches to train sub-semantic models, balancing model performance and training complexity. Secondly, we establish an optimization problem to maximize user quality of experience (QoE) through optimizing semantic compression ratios and D2D multicast channel selection. We analyze it from a game theory perspective, modelling the maximization of average QoE for all cell users as a potential game. Finally, we design a joint semantic compression ratio selection and channel allocation strategy based on the spatial adaptive (JSCSA) algorithm to achieve Nash Equilibrium (NE) and demonstrate the convergence of the algorithm. Simulation results confirm the superiority of the proposed algorithm.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3529763", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4406354744", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["semantic communication"]}
{"title": "SemAudio: Semantic Communication for Audio Streaming Transmission", "authors": ["Wenjun Xu", "Hao Wei", "Fengyu Wang", "Wanli Ni", "Ping Zhang"], "year": 2025, "publication_date": "2025-08-14", "doi": "10.1109/tvt.2025.3598973", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "2411-2425", "abstract": "Semantic communication (SemCom) has been regarded as a promising candidate technology for future intelligent wireless networks by transmitting semantic features. Meanwhile, the rapidly increasing real-time applications bring high demands for audio streaming data processing and exchange. Inspired by this, we propose a SemCom system for audio streaming transmission, named SemAudio. To effectively extract the semantic features of streaming audio, we develop a streaming enhanced Transformer (SET) network to serve as the streaming semantic codec. Specifically, the flexible mask attention network and enhanced memory slot are incorporated in SET to improve the streaming audio reconstruction quality while mitigating the computational complexity. Then, to alleviate the channel noise, we jointly design the semantic and channel coding by integrating the knowledge in both the time and frequency domains. Extensive experimental results demonstrate that the proposed SemAudio outperforms conventional methods in different audio types. Our scheme achieves a better quality-latency tradeoff than traditional communications. Moreover, due to the proposed mask attention strategy, SemAudio can flexibility adjust the latency and adapt to the non-streaming mode.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3598973", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "image transmission vehicular network", "video transmission vehicular network", "feature transmission edge inference", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4413462886", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "SNR Adaptive Attention Joint Source Channel Coding for Enhanced Image Transmission in Wireless Vehicular System", "authors": ["Haoze Chang", "Lin Ma", "Danyang Qin"], "year": 2025, "publication_date": "2025-08-29", "doi": "10.1109/tvt.2025.3604058", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "2734-2748", "abstract": "Reliable image transmission is critical for applications such as autonomous driving, collision avoidance, and real time traffic monitoring in the field of vehicles. However, extreme and dynamic vehicular environments impose significant challenges on reliable image transmission. Traditional communication methods that separately design source and channel coding fail to fully exploit the signal-to-noise ratio (SNR) information in vehicular channels and lead to suboptimal performance. Although recent deep learning (DL) based approaches have shown promising results in enhancing image transmission, they are often constrained by high storage requirements and limited adaptability to the highly variable channel conditions in vehicular environments. In this paper, a novel DL-based SNR adaptive attention joint source channel coding (SAA-JSCC) framework tailored for vehicular wireless communication systems is proposed. The proposed SAA-JSCC dynamically adjusts encoding and decoding strategies in real time by leveraging instantaneous SNR feedback, thereby optimizing transmission efficiency and reliability. A deep neural network is employed to directly map input RGB images to complex-valued baseband samples, while the receiver integrates the decoding and decision-making processes within its neural architecture. Comprehensive simulations under diverse vehicular channel conditions demonstrate that the SAA-JSCC approach significantly outperforms existing JSCC methods in terms of Peak Signal-to-Noise Ratio (PSNR) for image transmission, particularly under low SNR scenarios prevalent in vehicular environments. Furthermore, the proposed method delivers significant PSNR gain with a modest increase in FLOPs and negligible parameter growth, while remaining far smaller model size than prior deep baselines, thereby facilitating more efficient and scalable real time communication in next-generation vehicular networks.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3604058", "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", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "joint source channel coding video transmission"], "openalex_id": "https://openalex.org/W4413822507", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Image Transmission in Wireless", "joint source channel coding", "Joint Source Channel Coding", "JSCC"]}
{"title": "Spiking Semantic Communication for Feature Transmission With HARQ", "authors": ["Mengyang Wang", "Jiahui Li", "Mengyao Ma", "Xiaopeng Fan"], "year": 2025, "publication_date": "2025-02-11", "doi": "10.1109/tvt.2025.3540603", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "6", "pages": "10035-10040", "abstract": "In Collaborative Intelligence (CI), the Artificial Intelligence (AI) model is divided between the edge and the cloud, with intermediate features being sent from the edge to the cloud for inference. Several deep learning-based Semantic Communication (SC) models have been proposed to reduce feature transmission overhead and mitigate channel noise interference. Previous research has demonstrated that Spiking Neural Network (SNN)-based SC models exhibit greater robustness on digital channels compared to Deep Neural Network (DNN)-based SC models. However, the existing SNN-based SC models require fixed time steps, resulting in fixed transmission bandwidths that cannot be adaptively adjusted based on channel conditions. To address this issue, this paper introduces a novel SC model called SNN-SC-HARQ, which combines the SNN-based SC model with the Hybrid Automatic Repeat reQuest (HARQ) mechanism. SNN-SC-HARQ comprises an SNN-based SC model that supports the transmission of features at varying bandwidths, along with a policy model that determines the appropriate bandwidth. Experimental results show that SNN-SC-HARQ can dynamically adjust the bandwidth according to the channel conditions without performance loss.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3540603", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "image transmission vehicular network", "video transmission vehicular network", "feature transmission edge inference", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4407361614", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2310.08804", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Task-Oriented Multi-User Semantic Communication Based on Deep Over-the-Air Computation", "authors": ["Shuangying Wei", "Chunyan Feng", "Caili Guo", "Biling Zhang", "Jiujiu Chen"], "year": 2025, "publication_date": "2025-05-01", "doi": "10.1109/tvt.2025.3566397", "venue": "IEEE Transactions on Vehicular Technology", "volume": "74", "issue": "9", "pages": "14343-14357", "abstract": "The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel multi-user semantic communication framework based on deep over-the-air computation called SC-DAC to tackle this challenge. By integrating communication and computation through a deep over-the-air computation (AirComp) encoder, SC-DAC is able to utilize the superposition of multiple access channels, facilitating multi-user information fusion during transmission and eliminating the need for multi-user detection at the receiver. Further, to achieve the optimal task results, the semantic encoders, decoders, and AirComp encoder are jointly optimized, where stochastic gradient descent (SGD) algorithm and deep double-Q network (DDQN) are employed to optimize the encoder/decoder parameters. The simulation results demonstrate that as the number of users increased, the performance of the system remains relatively stable and shows potential in resource utilization. What's more, when applied to the AI-based tasks such as multi-user multimodal action recognition and semantic segmentation, SC-DAC enhances the accuracy of action recognition by 26% compared with traditional methods and by about 30%-50% compared with other multi-user semantic communication methods under conditions of low signal-to-noise ratio (SNR), while it enhances the accuracy of semantic segmentation by 0.22 to 0.32 compared to other methods", "publisher_url": "https://doi.org/10.1109/tvt.2025.3566397", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4409992082", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Zero-Delay Communications: A Predictive Semantic Communication Scheme Based on PCVAE", "authors": ["Xi Wang", "Xuefei Li", "Hongyuan Wang", "Neng Ye", "Jianping An"], "year": 2025, "publication_date": "2025-09-04", "doi": "10.1109/tvt.2025.3606126", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "2", "pages": "3481-3485", "abstract": "By eliminating semantic redundancy in source information, semantic communication can achieve performance beyond the Shannon limit. In this paper, we introduce an innovative predictive semantic communication paradigm that utilizes the spatio-temporal correlations inherent in signals, to overcome the intrinsic latency associated with wireless transmission and processing, thus approximating the ideal of zero-delay communication. The methodology initiates with the semantic compression of images using the clustering vector quantization autoencoder semantic compression (CAESC) method, which significantly reduces the data volume transmitted while maintaining minimal impact on reconstruction quality. Subsequently, the scheme employs a convolutional long short-term memory (ConvLSTM) network for predicting image sequences. Empirical simulations show a 48- fold reduction in data transmission volume with minimal loss in reconstruction quality. Furthermore, our predictive methodology significantly improves the learned perceptual image patch similarity (LPIPS), showing about a 43% increase during one constant delay period over conventional approaches.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3606126", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4413979628", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Zero-Shot Semantic Communication With Multimodal Foundation Models", "authors": ["Jiangjing Hu", "Haotian Wu", "Wenjing Zhang", "Fengyu Wang", "Wenjun Xu", "Hui Gao", "Denız Gündüz"], "year": 2025, "publication_date": "2025-11-14", "doi": "10.1109/tvt.2025.3632893", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "5", "pages": "8759-8764", "abstract": "Most existing semantic communication (SemCom) systems use deep joint source-channel coding (DeepJSCC) to en code task-specific semantics in a goal-oriented manner. However, their reliance on predefined tasks and datasets significantly limits their flexibility and generalizability in practical deployments. Multi-modal foundation models provide a promising solution by generating universal semantic tokens. Inspired by this, in this paper, we propose SemCLIP, a zero-shot SemCom framework leveraging the contrastive language-image pre-training (CLIP) model. CLIP-generated image tokens are transmitted in Sem CLIP under low bandwidth and challenging channel conditions, facilitating diverse zero-shot applications. Specifically, we propose a DeepJSCC scheme for efficient CLIP token encoding. To mit igate potential degradation caused by compression and channel noise, a multi-modal transmission-aware prompt learning (TAPL) mechanism is designed at the receiver, which adapts prompts based on transmission quality, enhancing system robustness and channel adaptability. Simulation results demonstrate that SemCLIP outperforms the baselines, achieving a 41% improve ment in zero-shot performance at low signal-to-noise ratios. Meanwhile, SemCLIP reduces bandwidth usage by more than 50-fold compared to alternative image transmission methods, demonstrating the potential of foundation models towards a generalized, task-agnostic SemCom solution.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3632893", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W4416294405", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2502.18200", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["deep joint source-channel", "DeepJSCC", "semantic communication", "Semantic Communication"]}
{"title": "A Deep Learning-Based Myerson Auction for Semantic Communications in Vehicular Edge Computing Networks", "authors": ["Die Wang", "Linjiang Shen", "Hongzhi Cui", "Yan Xu", "Yunjian Jia"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3737785", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3737785", "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", "vehicular semantic communication", "V2X semantic communication", "semantic-aware vehicular network", "feature transmission edge inference", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7214302491", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Adaptive Resource Allocation and Semantic Priority Control for Multi-UAV-Aided Relaying Systems", "authors": ["Le-Tuan Duy Nguyen", "Nguyen Cong Luong", "Thai-Hoc Vu", "Tiến Hoa Nguyễn", "Xingwang Li"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3701020", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-12", "abstract": "In this paper, we investigate a novel Semantic Communication (SemCom)-enabled multi-Unmanned Aerial Vehicle (UAV)-aided relaying system operating within a multi-time slot framework, each comprising two distinct phases. In the initial phase, the UAVs perform user association and receive data from their associated users while capturing images and extracting semantic triplets from the images. In the subsequent phase, the UAVs forward both the accumulated user data and extracted triplets to a remote base station (BS). While user association can be effectively managed using the Signal-to-Interference-plus Noise Ratio (SINR) metric, data transmission in the second phase encounters significant challenges due to the limited number of available channels, as well as the heterogeneous nature and varying priorities of user data and semantic triplets. To address this, we formulate an optimization problem aimed at jointly optimizing channel association, transmit power, and the priority level thresholds for triplet selection for the UAVs. The problem is inherently non-convex with time-varying parameters including the user data, contextual information, scene graphs, fading channels and distances between the users and UAVs. To tackle this, we propose a multi-agent reinforcement learning (MARL) algorithm with low computation complexity. This approach models the system as a decentralized partially-observable Markov game (Dec-POMDP), where UAVs learn optimal policies, guided by reward functions that prioritize high-QoS relay data. Simulations using the Open Images V6 dataset show our MARL algorithm improves relay data transmission by up to 18% and enables more triplet transmissions by reducing average priority thresholds by up to 50% (vs. single-agent learning). Notably, a system-centric mode with enhanced UAV coordination significantly outperforms a user-centric approach.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3701020", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7163679813", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning", "authors": ["Fangzhou Zhao", "Yao Sun", "Jianglin Lan", "Lan Zhang", "Xuesong Liu", "Muhammad Ali Imran"], "year": 2026, "publication_date": "2026-01-15", "doi": "10.1109/tvt.2026.3653997", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "14356-14367", "abstract": "Effective path planning is fundamental to the coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems, particularly in applications such as surveillance, navigation, and emergency response. Combining UAVs' broad field of view with UGVs' ground-level operational capability greatly improve the likelihood of successfully achieving task objectives-such as locating victims, monitoring target areas, or navigating hazardous terrain. In complex environments, UAVs need to provide precise environmental perception information for UGVs to optimize their routing policy. However, due to severe interference and non-line-of-sight conditions, wireless communication is often unstable in such complex environments, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework to enhance UAV/UGV cooperative path planning under unreliable wireless conditions. Unlike traditional methods that transmit raw data, SemCom transmits only the key information for path planning, reducing transmission volume without sacrificing accuracy. The proposed framework is developed by defining key semantics for path planning and designing a transceiver for meeting the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3653997", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7124460860", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2510.06901", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "BidDeepSC-1.58b: 1.58-Bit Bidirectional Slimmable Semantic Communication System", "authors": ["Tung Son Do", "Thanh Phung Truong", "Quang Tuan Do", "Dongwook Won", "Wonjong Noh", "Sungrae Cho"], "year": 2026, "doi": "10.1109/tvt.2025.3622754", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "4", "pages": "6254-6269", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2025.3622754", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "record_type": "journal-article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Channel Assignment for a Polar Code-Based Semantic Communication System", "authors": ["Zhixiang Qiao", "Yuanhao Sun", "Kairong Ma", "Yixuan Fan", "Runze Cheng", "Chengsi Liang", "Lei Zhang", "Muhammad Ali Imran"], "year": 2026, "publication_date": "2026-03-16", "doi": "10.1109/tvt.2026.3674111", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "17095-17107", "abstract": "Semantic communication (SemCom) has been deemed as a promising paradigm focusing on semantic meaning rather than raw data for efficient and robust information transmission. While machine learning (ML) has advanced semantic processing capabilities, practical SemCom system deployment requires to achieve a high utilization of wireless channels. Traditional channel assignment schemes operate independently of semantic content, i.e., all transmitted bits are treated equally, leading to inadequate protection of important semantic information and a high semantic error rate under adverse channel conditions. In this paper, we propose a channel assignment approach for polar code-based SemCom that aligns channel polarization with semantic importance. First, to accurately evaluate semantic importance, gaze-inspired attention modeling is employed to calculate the semantic contribution of image regions, together with the consideration of semantic correlation between entities. Then, based on the calculated semantic importance and reliability of the polarized channel, we formulate the polarized channel assignment as a 0-1 programming problem and propose a solution containing two assignment strategies for different transmission scenarios. The simulation results demonstrate that, compared to traditional polarized channel assignment approaches, our approach improves semantic transmission efficiency and robustness by effectively matching semantic entities with appropriate channel resources.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3674111", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7136570196", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://eprints.gla.ac.uk/387615/1/387615.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication", "semantic transmission"]}
{"title": "Communicate Less, Synthesize the Rest: Latency-Aware Intent-Based Generative Semantic Multicasting With Diffusion Models", "authors": ["Xinkai Liu", "Mahdi Boloursaz Mashhadi", "Li Qiao", "Yi Ma", "Rahim Tafazolli", "Mehdi Bennis"], "year": 2026, "publication_date": "2026-02-02", "doi": "10.1109/tvt.2026.3660013", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "13722-13737", "abstract": "Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aware generative semantic multicasting framework utilizing pre-trained diffusion models. In the proposed framework, the transmitter decomposes the source signal to multiple semantic classes based on the multi-user intent, i.e. each user is assumed to be interested in details of only a subset of the semantic classes. To better utilize the wireless resources, the transmitter sends to each user only its intended classes, and multicasts a highly compressed semantic map to all users over shared wireless resources that allows them to locally synthesize the other classes, i.e. non-intended classes, utilizing pre-trained diffusion models. The signal retrieved at each user is thereby partially reconstructed and partially synthesized utilizing the received semantic map. We design a communication/computation-aware scheme for per-class adaptation of the communication parameters, such as the transmission power and compression rate to minimize the total latency of retrieving signals at multiple receivers, tailored to the prevailing channel conditions as well as the users' reconstruction/synthesis distortion/perception requirements. The simulation results demonstrate significantly reduced per-user latency compared with non-generative and intent-unaware multicasting benchmarks while maintaining high perceptual quality of the signals retrieved at the users. For a typical setup of multicasting street scene images to 10 users, our proposed framework achieves a$15.4\\%$reduction in per-user latency at a fixed power budget, or equivalently$50\\%$reduction in the transmission power required to achieve a fixed per-user latency, compared with non-generative multicasting.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3660013", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7127091128", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2411.02334", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["semantic communication"]}
{"title": "CSI Feedback via Joint Source-Channel Coding and Modulation: A Comprehensive Study for 6G Realization", "authors": ["Nan Jiang", "Younsun Kim", "B X Yang", "Hang Qi", "Yu Xiao", "Yupeng Cui", "Feifei Sun", "Chen Qian", "Bin Yu", "Chengjun Sun", "Hyoung-ju Ji", "Min Jang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3703766", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-17", "abstract": "In modern wireless systems, the process of acquiring channel state information (CSI) has become increasingly critical yet challenging, driven by the conflicting needs of expanding antenna arrays and the pursuit of high spectral efficiency. Recent research particularly leveraging joint source-channel coding and modulation (JSCM) via autoencoder architectures, has demonstrated initial promise in significantly enhancing CSI feedback effectiveness. However, a substantial gap persists between algorithm research and practical realization, particularly concerning real-world validation and systematic design methodologies. This article introduces a comprehensive framework enabling JSCM-based CSI feedback, which encompasses a learning methodology robust against wireless channel imperfection and protocol designs tailored for seamless integration into practical cellular systems. A hardware proof-of-concept (PoC) platform is developed and deployed, where experimental results substantiate the proposed method's superiority in realistic wireless environments. Furthermore, by synergizing technical and protocol innovations, this article resolves three critical challenges impeding the deployment of JSCM-based CSI feedback in future 6G networks: variant resource allocation, low peak-to-average power ratio (PAPR) waveforms design, and accurate channel quality indication (CQI) estimation. Equipped with these enabling techniques, the proposed JSCM-based CSI feedback solution is positioned for 6G adoption, demonstrating substantial throughput improvements over conventional separate source and channel coding (SSCC) implementations.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3703766", "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", "joint source channel coding image transmission", "joint source channel coding video transmission"], "openalex_id": "https://openalex.org/W7164820088", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "joint source-channel coding"]}
{"title": "Design and Demonstration of a Visible Light Based Semantic Communication System", "authors": ["Kairong Ma", "Hanaa Abumarshoud", "Shuheng Hua", "Muhammad Ali Imran", "Yao Sun"], "year": 2026, "publication_date": "2026-01-30", "doi": "10.1109/tvt.2026.3659747", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "13683-13694", "abstract": "While visible light communication (VLC) is emerging as a crucial technology for meeting high traffic demand in future network, the intrinsically limited modulation bandwidth of light-emitting-diodes (LEDs) necessitates innovative approaches to enhance data throughput. Semantic communication (SemCom), which focuses on transmitting the meaning of data rather than raw bits, is a perfect candidate to overcome this bottleneck in VLC. In this paper, we investigate how to build a visible light based SemCom (VL-SemCom) system, where non-orthogonal multiple access (NOMA) is leveraged to accommodate multiple users. First, we mathematically characterize the model of a VL-SemCom system, which simultaneously takes into account the SemCom transmission features, the NOMA multi-user access principle, and the physical layer constraints of VLC. Second, we address the core issue of power allocation in the VL-SemCom system with the aim to maximize the semantic throughput while considering user fairness and practical constraints of VLC. Finally, we build a preliminary hardware demonstration platform to achieve SemCom with the proposed power allocation strategy over real VLC links. The simulation and preliminary demo results together validate that our proposed power allocation strategy can effectively improve the performance of semantic throughput while satisfying the VLC constraints, and provide a valuable reference for the future practical deployment of VL-SemCom networks.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3659747", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7126180741", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://eprints.gla.ac.uk/376653/3/376653.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication"]}
{"title": "Design of a Speech Enhancement-Based Deep Semantic Autoencoder for Wireless Communications", "authors": ["Xiaoling Han", "Bin Lin", "Nan Wu", "Tony Q. S. Quek"], "year": 2026, "publication_date": "2026-01-28", "doi": "10.1109/tvt.2026.3658080", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "13450-13462", "abstract": "Speech communication plays a crucial role in the Internet of Things and autonomous networks. However, traditional communication systems suffer from insufficient semantic extraction and limited noise suppression capability. To address these issues, we propose a Speech enhancement-based Deep Semantic Autoencoder (S-DSA) for wireless communications. It consists of a dual-branch semantic encoder, a channel encoder, a channel decoder, and two semantic decoders, enabling joint source-channel coding for robust transmission. A key novelty lies in an adaptive semantic noise decoupling mechanism that disentangles target speech semantics from noise representations through an interaction module, thus maintaining semantic consistency under low SNR conditions. Furthermore, a joint training strategy optimizes speech reconstruction and noise estimation, while a lightweight testing strategy reduces complexity by disabling the noise-decoding path. the proposed S-DSA enables more effective separation between speech and noise, and yields higher robustness as well as better reconstruction quality. These findings highlight the potential of the proposed S-DSA as a practical solution for next-generation intelligent communication systems.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3658080", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7125963566", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["joint source-channel coding"]}
{"title": "Design of Multi-UAV Cooperative Deep Semantic Autoencoders for Communication Networks", "authors": ["Xiaoling Han", "Bin Lin", "Nan Wu", "Tony Q. S. Quek"], "year": 2026, "publication_date": "2026-03-13", "doi": "10.1109/tvt.2026.3673755", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "17063-17077", "abstract": "Driven by the rapid development of the low-altitude economy, Uncrewed Aerial Vehicle (UAV) networks have become increasingly important for executing tasks such as search, rescue, and monitoring. However, under bandwidth-constrained scenarios, UAV networks still face significant challenges in task execution efficiency and intelligent processing capabilities. To address these issues, we propose Multi-UAV Cooperative Deep Semantic Autoencoders (MC-DSA) for communication networks. The architecture comprises one Task UAV (T-UAV) and multiple Support UAVs (S-UAVs), which jointly enhance communication efficiency and data-processing capability via semantic communication and cooperation. Specifically, we introduce a semantic matching and source selection mechanism, enabling the proposed MC-DSA to handle low-quality images while ensuring effective utilization of high-quality images. The T-UAV broadcasts a task query, and each S-UAV computes the semantic similarity between its local image and the query. Communication links are established only when the similarity exceeds a predefined threshold. The training algorithm optimizes network parameters through gradient descent, improving semantic feature extraction accuracy. The testing algorithm evaluates UAV collaboration efficiency and communication quality. Experimental results demonstrate that, compared with benchmarks, the proposed MC-DSA improves image semantic segmentation accuracy while maintaining task execution efficiency. As a result, it reduces redundant transmissions and offers an effective solution for multi-UAV communication networks.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3673755", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7135209636", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication"]}
{"title": "Efficient Multimodal Task-Oriented Communication with Dynamic Modality Selection", "authors": ["Hang Zhou", "Yiyang Ni", "Long Shi", "J F Li", "Yuwen Qian", "Shi Jin"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3696958", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-5", "abstract": "Task-oriented communication leverages semantic information for efficient transmission by focusing on meanings relevant to downstream tasks rather than exact symbol recovery. In multimodal scenarios, most existing approaches adopt static fusion, where all modalities are always processed jointly. However, this often leads to redundant communication overhead, since some samples can be accurately inferred from a single modality, whereas more ambiguous cases require additional modalities. To address this issue, we propose a multimodal task-oriented communication system based on dynamic modality selection, where conditional mutual information (CMI) is used to assess the incremental value of each modality. A gating network leverages these estimates to select task-relevant modality combinations, achieving adaptive resource allocation. Experiments on movie genre classification and sentiment analysis show that the proposed system sustains competitive accuracy while reducing both computation and transmission costs by over30%, providing an interpretable and efficient solution for resource-constrained task-oriented communication.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3696958", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "task-oriented communication", "goal-oriented communication", "task-oriented vehicular communication", "goal-oriented vehicular communication"], "openalex_id": "https://openalex.org/W7162447744", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Task-Oriented Communication", "task-oriented communication", "Task-oriented communication"]}
{"title": "Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments", "authors": ["Fangyu Liu", "Peiwen Jiang", "Wenjin Wang", "Chao-Kai Wen", "Shi Jin", "Jun Zhang"], "year": 2026, "publication_date": "2026-03-31", "doi": "10.1109/tvt.2026.3679047", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "17546-17561", "abstract": "Foundation model-based semantic transmission has recently shown great potential in wireless image communication. However, existing methods exhibit two major limitations: (i) they fail to account for the varying importance of semantic components across different downstream tasks, and (ii) they do not sufficiently exploit wireless domain knowledge, which leads to limited robustness under dynamic channel conditions. To overcome these challenges, this paper proposes a foundation model-based adaptive semantic image transmission system tailored for dynamic wireless environments, such as autonomous driving. The proposed system decomposes each image into a semantic segmentation map and a compressed representation, enabling task-aware prioritization of critical objects and fine-grained textures. A task-adaptive precoding mechanism then allocates radio resources according to the semantic importance of extracted features. To support accurate precoding, a channel estimation knowledge map (CEKM) is constructed using a conditional diffusion model that integrates user position, velocity, and sparse channel samples to train scenario-specific lightweight estimators. At the receiver, a ControlNet-guided diffusion model reconstructs high-quality images from received semantics, enhancing robustness to channel impairments. Extensive experiments conducted on multiple datasets and channel models, including an unseen 3GPP clustered delay line (CDL) channel model, demonstrate the strong generalization and scalability of the proposed framework. Robust performance is further validated under dynamic channel quality, channel estimation mismatch, and imperfect channel state information (CSI), yielding consistent gains in perceptual quality (e.g., SSIM, LPIPS, FID), task accuracy (IoU), and transmission efficiency. These results highlight the effectiveness of integrating task-aware semantic decomposition, scenario-adaptive channel estimation, and diffusion-based reconstruction for robust semantic transmission under dynamic environments.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3679047", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7147727931", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic transmission"]}
{"title": "FQ-LISCS: Fully Quantized Lightweight Image Semantic Communication System for Edge Devices", "authors": ["Congduan Li", "Jiahang Zhong", "Peng Liu", "Nanfeng Zhang", "Jingfeng Yang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3697594", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "The rise of the Internet of Things (IoT) and edge computing increases the demand for efficient image transmission under strict latency, memory, and power budgets. While learned semantic communication can reduce bandwidth by transmitting task-relevant representations rather than raw pixels, state-of-the-art models are difficult to deploy on resource-constrained devices due to their reliance on floating-point computation and heavy memory footprint. To bridge this gap, we propose a fully quantized lightweight image semantic communication system (FQ-LISCS), which constructs a full-path integer-only inference pipeline for semantic image transmission. Specifically, FQ-LISCS applies 8-bit integer (INT8) quantization to linear operators and introduces efficient integer approximations for key nonlinearities, enabling end-to-end deployment without floating-point operations during inference. Experimental results demonstrate that FQ-LISCS preserves transmission quality close to full-precision baselines, with a peak signal-to-noise ratio (PSNR) drop of only 0.18 dB compared with wireless image transmission transformer (WITT) on the Kodak test set, while substantially reducing hardware resource consumption.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3697594", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "feature transmission edge inference", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7162636199", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication", "wireless image transmission"]}
{"title": "Generative AI-Driven Semantic Communication Framework for NextG Wireless Network", "authors": ["Avi Deb Raha", "Md. Shirajum Munir", "Apurba Adhikary", "Mrityunjoy Gain", "Yu Qiao", "Choong Seon Hong"], "year": 2026, "publication_date": "2026-03-04", "doi": "10.1109/tvt.2026.3670390", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "18781-18786", "abstract": "This work designs a novel semantic communication (SemCom) framework for next-generation wireless networks to tackle high bandwidth consumption, latency, and degraded quality of service (QoS). These challenges, caused by transmitting vast amounts of unnecessary data, hinder real-time applications like intelligent transportation systems (ITS), metaverse, and mixed reality. The proposed framework aims to reduce this communication overhead and maintain QoS for these emerging systems. First, an intelligent semantic transmitter is designed to capturemeaningfulinformation (e.g.,roadsideimage in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introducedat the receiverfor building the SemComframeworkto reconstruct and denoise the received semantic dataframes. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintainhigh-qualityreconstruction under different signal-to-noise (SNR) channel conditions. To support edge deployment, a lightweight knowledge-distilled GAN is proposed for efficient reconstruction with minimal quality loss. Finally, we have evaluated the proposed semantic communication (SemCom) frameworkin areal-world 6 G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate theeffectivenessof the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 89.45% data reduction in communication.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3670390", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7133532319", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2310.09021", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Goal-Oriented Semantic Communication for Multi-Robots Odor Source Localization", "authors": ["Wenjie Liu", "Yansha Deng"], "year": 2026, "publication_date": "2026-03-04", "doi": "10.1109/tvt.2026.3670422", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "18787-18792", "abstract": "In robotic odor source localization (OSL), the chemical plume is released by an odor source and dispersed by airflow. The cooperation of multi-robots can shorten the time to find and track the plume, so as to reduce the time to locate the odor source. Current research works on multi-robots OSL mainly focus on the control process, while the communication process is neglected. Although traditional bit-oriented communication framework can shed light on this issue, it may cause redundant information transmission and a waste of wireless resources. To this end, this paper designs a goal-oriented semantic communication framework (GSCF) for multi-robots OSL. Specifically, for the plume finding stage, we propose an age of information (AoI) based queue ranking mechanism to prioritize fresh uplink sensor data. For the plume tracking stage, we propose effectiveness-aware uplink sensor data retrieval, value of information (VoI) based queue ranking and AoI based sensor data discard mechanisms to guarantee the successful transmission of high-concentration sensor data. We further propose a maximum concentration gradient based gradient fusion (MCG-GF) algorithm to jointly utilize the effectiveness level information of multiple robots. Simulation results demonstrate that our proposed GSCF can reduce the OSL time by at least 11.12% when compared to the traditional bit-oriented multi-robots OSL approach, which verifies that prioritizing semantically critical sensor data can substantially improve localization efficiency.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3670422", "sources": ["OpenAlex full-journal metadata scan", "Crossref keyword search"], "matched_queries": ["full-journal metadata scan", "semantic communication", "semantic communications", "joint source-channel coding", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "joint source channel coding image transmission", "joint source channel coding video transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7133502357", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://kclpure.kcl.ac.uk/portal/en/publications/0b5728ae-4d1d-497e-86a9-5ae8b4eb5c2c", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "High-order Feature-Guided Efficient Semantic Communication System for Point Cloud Transmission", "authors": ["Liping Nong", "Guifa Zhu", "Jie Peng", "Jinling Liu", "J Chen", "Jiang Wang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3697613", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-16", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3697613", "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", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "video transmission vehicular network", "feature transmission edge inference", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7162792279", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Importance-Aware OFDM Transmission for VQ-VAE Based Digital Semantic Communications", "authors": ["Ming Lv", "Hao Chen", "Dan Wang", "Chen Qiu", "Guangyin Feng", "Nan Ma", "Xiaodong Xu"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3706678", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-16", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3706678", "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", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "video transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7165805676", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Integrated Sensing, Computing, Communication, and Control for Time-Sequence-Based Semantic Communications", "authors": ["Qingliang Li", "Bo Chang", "Weidong Mei", "Zhi Chen"], "year": 2026, "publication_date": "2026-03-06", "doi": "10.1109/tvt.2026.3671269", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "17772-17786", "abstract": "Real-time wireless control systems (WCSs) are fundamental to connected and autonomous vehicles as well as robotic applications, where ultra-reliable and low-latency communication is crucial for timely control information exchange. To achieve this purpose, we propose a novel time-sequence-based semantic communication paradigm, where an integrated sensing, computing, communication, and control (ISC3) architecture is developed to make sensible semantic inference (SI) for the control information over time sequences, enabling adaptive control of the robot. However, due to the causal correlations in the time sequence, the control information does not present the Markov property. To address this challenge, we compute the mutual information of the control information sensed at the transmitter (Tx) over different time and identify their temporal semantic correlation via a semantic feature extractor (SFE) module. By this means, highly correlated information transmission can be avoided, thus greatly reducing the communication overhead. Meanwhile, a semantic feature reconstructor (SFR) module is employed at the receiver (Rx) to reconstruct the control information based on the previously received one if the information transmission is not activated at the Tx. Furthermore, a control gain policy is also employed at the Rx to adaptively adjust the control gain for the controlled target based on several practical aspects such as the quality of the information transmission from the Tx to the Rx. We design the neural network structures of the above modules/policies and train their parameters by a novel hybrid reward multi-agent deep reinforcement learning framework. On-site experiments are conducted to evaluate the performance of our proposed method in practice, which shows significant gains over other baseline schemes.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3671269", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7134128296", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2505.03127", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Joint Beamforming for Multi-RIS-Aided Semantic Communication Systems", "authors": ["Daifu Yan", "Min Jia", "Qing Guo", "Tony Q. S. Quek"], "year": 2026, "publication_date": "2026-01-21", "doi": "10.1109/tvt.2026.3656692", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15084-15088", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3656692", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7125578570", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Joint Trajectory-Resource Optimization for UAV Semantic Communication Systems", "authors": ["Yue Sun", "Shuang Du", "Haibo Mei", "Qi Wang", "Chuanjie Liu", "Huan Ye"], "year": 2026, "publication_date": "2026-05-12", "doi": "10.1109/tvt.2026.3692564", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "9", "pages": "20661-20674", "abstract": "Nowadays, semantic communication (SC) has attracted intensive attention. By adopting an “Understand-before-transmit” approach supported by Deep Learning (DL), SC is capable of only sending semantic information (SI) rather than raw data, thereby conserving data transmission and communication costs without any degradation in performance. This makes SC particularly suitable for Unmanned Aerial Vehicle (UAV) communications, given that UAVs' limitations in size, weight, and power (SWaP). The SWaP constraint limits UAVs' airborne data collection capabilities, and affects the efficiency of large file transmissions, such as images and videos. This work explores the application of SC in UAV communications and proposes a UAV Semantic Communication (UAV-SC) system designed to expedite the wireless image transmission tasks of UAVs. Utilizing a Joint Source-Channel Coding (JSCC) method, we achieve image transmission based on SC between the UAV and the ground vehicles (GV). Subsequently, we develop a joint optimization algorithm for UAV trajectory and resource allocation to identify the near-optimal network configuration for UAV-SC, thereby enhancing overall communication performance. According to the numerical results from simulation, the joint algorithm supports the UAV-SC system working in an energy-efficient way and outperforms benchmark solutions.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3692564", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7160924974", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "JSCC", "semantic communication", "Semantic Communication", "wireless image transmission"]}
{"title": "Knowledge Graph Based Semantic Communication Networks With Deep Reinforcement Learning Enhancement", "authors": ["Shengping Zhou", "Ming Zeng", "Zhong Zheng", "Heng Liu"], "year": 2026, "publication_date": "2026-05-14", "doi": "10.1109/tvt.2026.3693533", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "9", "pages": "21588-21598", "abstract": "With the advancements in physical layer technologies such as source coding and channel coding techniques, the spectral efficiency of traditional communication systems has approached the Shannon limit. Exploring semantic communication paradigms becomes essential to address the challenges of transcending bit-level accuracy, meeting the demands of next-generation intelligent applications such as augmented reality, digital twins, and the tactile internet, which effectively conveys meaning and context in data transmission. Therefore, we propose a Self-Recovery Semantic Communication (SR-SC) architecture, which leverages Knowledge Graph Reasoning (KGR) encoder/decoder, a semantic restorer, and a semantic reducer to enhance the performance of communication networks. To model the correlations between semantic symbols, the KGR employs an efficient semantic encoding scheme based on TransR, enabling the system to represent semantic differences among entities and relationships as semantic distances in a low-dimensional vector space. Additionally, the semantic restorer and reducer are modeled as reinforcement learning tasks, with a Deep Q-Network (DQN) serving as the decision-making agent to incrementally restore and reduce semantic entities and relationships. This approach facilitates subsequent semantic reasoning. Finally, extensive simulation results on public datasets demonstrate that the proposed SR-SC architecture significantly reduces the semantic symbol error rate and enhances the effectiveness and reliability of the semantic communication networks.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3693533", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7161160932", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "LGVSC: A Large-Model-Driven Generative Video Semantic Communication Framework", "authors": ["Yu Ma", "Hang Yin", "Li Qiao", "Shuo Sun", "Zhen Gao", "Yin Xu", "Wenjun Zhang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3705309", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3705309", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "video transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7165149046", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "LLM-Enhanced Generative Semantic Communication and DRL Resource Management for UAV-Assisted Emergency Response", "authors": ["Ziji Guo", "Danpu Liu", "Z Zhang", "Q Wang", "Yue Wang", "Fangfang Yin"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3717035", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-16", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3717035", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7171434802", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Multi-Dimensional Multiple Access-Based Hybrid Semantic-Bit Communications for Individualized QoS Provisioning", "authors": ["Saba Habibi", "Jie Chen", "Fang Fang", "Xianbin Wang"], "year": 2026, "publication_date": "2026-01-27", "doi": "10.1109/tvt.2026.3658008", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15240-15245", "abstract": "Future wireless networks are expected to support massive heterogeneous devices with distinct capabilities in terms of semantic communications (SemCom) and bit-based communications (BitCom). However, conventional system designs face two fundamental limitations in enabling the coexistence of SemCom and BitCom users. First, existing schemes neglect the limited interference cancellation capability of BitCom users due to hardware constraints and reliance on single-domain resource utilization, leading to ineffective interference management. Second, conventional capacity-based metrics fail to capture user-specific service requirements and device heterogeneity, causing inefficient and non-adaptive resource utilization. To overcome these challenges, we propose a multi-dimensional multiple access-based hybrid semantic-bit communication (MDMA-HSBC) framework with two major contributions: (i) a dynamic switching mechanism at the base station that supports adaptive operation between SemCom and BitCom modes for dual-capable devices and enables the selection of the optimal access scheme for each user across multiple resource domains to ensure efficient interference management; and (ii) a cost-aware performance metric that jointly optimizes access schemes and communication modes to incorporate user-specific service demands and outperform capacity-driven designs. The resulting nonconvex problem is efficiently addressed through a heuristic matching algorithm, achieving significant performance gains over conventional baselines.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3658008", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7125829402", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication"]}
{"title": "Near-Field Integrated Sensing, Computing and Semantic Communication in Digital Twin-Assisted Vehicular Networks", "authors": ["Yinchao Yang", "Yahao Ding", "Jiaxiang Wang", "Zhaohui Yang", "Chen Zhu", "Zhaoyang Zhang", "Dusit Tao Niyato", "Mohammad Shikh‐Bahaei"], "year": 2026, "publication_date": "2026-04-13", "doi": "10.1109/tvt.2026.3683229", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "9", "pages": "20992-21008", "abstract": "The rapid development of digital twin (DT) technology has introduced transformative potential for vehicular networks, enabling real-time, high-fidelity virtual representations that can enhance safety, efficiency, and automation. However, realizing seamless DT synchronization in dynamic vehicular environments presents significant challenges, including the need for high data rates to support massive data transmission, precision sensing for accurate environmental mapping, and efficient resource management under strict latency and computational constraints. Addressing these challenges, this paper proposes an integrated sensing, computing, and semantic communication (ISCSC) framework specifically tailored for DT-assisted vehicular networks operating in the near-field (NF) regime. Leveraging a multi-user multiple-input multiple-output (MU-MIMO) configuration, each roadside unit (RSU) employs semantic communication to efficiently serve a subset of vehicles within the NF region while simultaneously utilizing millimetre-wave (mmWave) radar to detect all vehicles within its coverage area. We employ particle filtering at RSUs to achieve superior vehicle tracking accuracy. To optimize the system performance, we formulate a joint optimization problem balancing semantic communication rate and sensing accuracy with limited computational constraints. We developed a hybrid heuristic algorithm for vehicle-to-RSU assignments, followed by an alternating optimization approach for finding the optimal semantic extraction ratio and beamforming matrices. We extensively evaluate system performance through simulations, assessing the Crámer-Rao bound (CRB) for angle and distance estimation, semantic transmission rates, and computational resource utilization. Numerical results demonstrate that, under constrained resource conditions, the proposed ISCSC framework achieves a 20% improvement in transmission rate while maintaining the sensing accuracy of existing ISAC schemes.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3683229", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7154073813", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication", "semantic transmission"]}
{"title": "Perception Enhanced Multimodal Multitask Semantic Communication and Resource Management for UAV-Assisted ISAC Systems", "authors": ["Ziji Guo", "Danpu Liu", "Zhilong Zhang"], "year": 2026, "publication_date": "2026-01-05", "doi": "10.1109/tvt.2025.3650639", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "6", "pages": "11051-11065", "abstract": "Recent advances in integrated sensing and communication (ISAC) unmanned aerial vehicles (UAVs) have enabled their widespread deployment in critical applications such as emergency management. Existing approaches still face challenges in energy efficiency, real-time adaptability, and service latency. To address these limitations, a perception enhanced multimodal multitask semantic communication (PE-MMSC) framework is first proposed, and a task-oriented resource optimization model is established on this foundation to maximize comprehensive quality of experience (QoE) in this paper. Specifically, PE-MMSC leverages a perception-enhanced attention mechanism to achieve semantic enhancement of critical features and more effective multimodal fusion guided by preliminary coarse classification results. For the resource optimization model, reinforcement learning is employed to address two subproblems: trajectory planning and resource management, and a semantic importance (SI) feedback mechanism is proposed to develop SI SI-guided double deep Q-network(SI-DDQN), which can make adaptive resource allocation decisions for important data with semantic awareness. Experimental results demonstrate that the proposed PE-MMSC system achieves 5%–10% higher target classification accuracy compared to conventional systems while maintaining comparable data reconstruction quality with acceptable computational overheads, and the SI-DDQN achieves nearly two to three times greater QoE improvement than traditional algorithms.", "publisher_url": "https://doi.org/10.1109/tvt.2025.3650639", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7118183964", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Quantum Communication for Semantic Communication Based Image Transmission", "authors": ["Udara Jayasinghe", "Prabhath Samarathunga", "Nimesh Pollwaththage", "Thanuj Fernando", "Anil Fernando"], "year": 2026, "publication_date": "2026-02-11", "doi": "10.1109/tvt.2026.3663843", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "14052-14061", "abstract": "The growing demand for reliable image transmission in noisy vehicular networks exposes the limitations of classical communication, which often suffers from bandwidth constraints and susceptibility to noise. Classical semantic communication addresses this by focusing on essential information, but it still struggles to maintain consistent image quality under adverse channel conditions. To address these limitations, this study proposes a quantum communication system for semantic communication-based image transmission by leveraging quantum superposition. The system first uses a semantic encoder to generate a compact latent representation. This representation is subsequently processed by a channel encoder and mapped into quantum superposition states using a quantum encoder. The resulting quantum states are sent through a noisy quantum channel, where they are measured and decoded at the receiver to recover the corresponding classical bitstream. The bitstream is then channel decoded to recover the latent vector, which is used by a semantic decoder to reconstruct the image. Results from the experiments confirm that the proposed system outperforms classical semantic communication systems, achieving a peak signal-to-noise ratio (PSNR) of up to 28.55 dB, a structural similarity index (SSIM) of up to 0.7996, a universal quality index (UQI) of up to 0.7836, and perfect classification accuracy at channel SNR levels as low as 6 dB, as evaluated by a convolutional neural network (CNN). These results highlight the system's effectiveness in task-oriented communication and show that combining quantum and semantic communication enhances image quality and reliability in challenging vehicular scenarios.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3663843", "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", "wireless image transmission", "wireless video transmission", "semantic modulation", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "task-oriented vehicular communication", "goal-oriented vehicular communication", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7128640392", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://strathprints.strath.ac.uk/95568/1/Jayasinghe-etal-IEEE-TVT-2026-Quantum-communication-for-semantic-communication-based-image-transmission.pdf", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication", "task-oriented communication"]}
{"title": "Random Access for Semantic Transmission Under Finite Buffer and Retransmission in Vehicular Networks", "authors": ["Yuyi Sun", "Tingting Xiao", "Fangyuan Xing", "Shibo He"], "year": 2026, "publication_date": "2026-02-20", "doi": "10.1109/tvt.2026.3666767", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15445-15450", "abstract": "The rapid advancement of autonomous driving brings large amount of data to transmit in vehicular networks, which increases the burden of channels. While semantic communication (SemCom) can alleviate bandwidth pressure by transmitting semantics rather than raw data, random access (RA) for SemCom may still suffer from collisions and delays due to resource competition. This indicates that, in current vehicular networks, a unified modeling and analysis framework integrating SemCom and access schemes is supposed to be designed and explored. In this correspondence, we propose a RA scheme named SVRA under the buffer and retransmission constraints, where the state characterizations are determined using Markov chain. Furthermore, we introduce the age of incorrect information (AoII) by considering the structure similarity index measure (SSIM). We compare SVRA with existing schemes, the results of which validate that SVRA has a better performance.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3666767", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "image transmission vehicular network", "video transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7130692091", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Transmission"]}
{"title": "Rate Controllable Semantic Communication for BEV Map Transmission", "authors": ["Yi-Xiang Wang", "Junjie Li", "Yang Tian", "Haofeng Sun", "Hui Tian", "Jingyi Cao"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3716351", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3716351", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "goal-oriented vehicular communication", "image transmission vehicular network", "video transmission vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7170164901", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "Rate Maximization for Fluid Antenna System Assisted Semantic Communication", "authors": ["Siyun Liang", "Chen Zhu", "Zhaohui Yang", "Changsheng You", "Dusit Tao Niyato", "Kai‐Kit Wong", "Zhaoyang Zhang"], "year": 2026, "publication_date": "2026-02-16", "doi": "10.1109/tvt.2026.3664892", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15329-15333", "abstract": "In this paper, we investigate the problem of rate maximization in a fluid antenna system (FAS) assisted semantic communication system. In the considered model, a base station (BS) with multiple static antennas employs semantic extraction techniques to compress the data ready to be sent to a user. The user equipped with a fluid antenna is located in the near field coverage region of the BS. Our aim is to jointly optimize the transmit beamforming and the semantic compression rate at the BS, as well as the selection of activated ports in FAS, to maximize the equivalent transmission ratio under a specific power budget. We design an alternating algorithm to solve the problem, where we obtain the optimal semantic compression ratio is in closed form at each step. Simulation results validate the effectiveness of the proposed algorithm.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3664892", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7129059735", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2506.22943", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Communication"]}
{"title": "Robust Image Semantic Communication via Wavelet-Attentive JSCC Under Adversarial and Channel Perturbations", "authors": ["Zhen Huang", "Yunjian Jia", "Wanli Wen", "Qingqing Li", "Liang Liang", "Xinyue Liu"], "year": 2026, "publication_date": "2026-01-16", "doi": "10.1109/tvt.2026.3655029", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15193-15197", "abstract": "Semantic communication receives significant attention for its ability to efficiently transmit meaningful information rather than pursuing bit-level accuracy, thereby minimizing resource consumption. However, in addition to wireless channel interference, this paradigm is vulnerable to semantic impairments caused by adversarial attacks. Recent advances in deep learning-based Joint Source-Channel Coding (JSCC) significantly improve communication robustness against physical channel noise but often overlook semantic-level perturbations. In this paper, we propose a wavelet transform frequency-domain attention feature (WTAF) module, which employs multilevel wavelet decomposition to effectively separate semantic structures from high-frequency adversarial noise. It also incorporates a channel-aware attention mechanism that dynamically adapts semantic feature extraction based on real-time channel conditions. We integrate WTAF to design a JSCC model for robust image semantic communication under adversarial interference and wireless channel interference conditions. Simulation results show that the proposed JSCC scheme outperforms existing JSCC models and traditional separate source-channel coding in terms of reconstruction quality, semantic integrity, and overall robustness, particularly under severe semantic degradation.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3655029", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "joint source channel coding image transmission", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7124426079", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Joint Source-Channel Coding", "JSCC", "semantic communication", "Semantic communication", "Semantic Communication"]}
{"title": "Robust MIMO Semantic Communication With Imperfect CSI via Knowledge Distillation", "authors": ["Mingze Gong", "Shuoyao Wang", "Shijian Gao", "Jia Yan", "Suzhi Bi"], "year": 2026, "publication_date": "2026-02-06", "doi": "10.1109/tvt.2026.3661738", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15281-15286", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3661738", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7128304526", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "SAGEComm: Semantic-Aware Generation and Encoding for Next-Gen Communication", "authors": ["Shih-Yu Chang", "Yi‐Chih Kao", "Zhu Han"], "year": 2026, "publication_date": "2026-03-23", "doi": "10.1109/tvt.2026.3676671", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "17330-17346", "abstract": "Vehicular technology encompasses the electronic systems, communication protocols, and control mechanisms that enable modern transportation systems. In this context, we propose SAGEComm-Semantic-Aware Generation and Encoding for Communication-a next-generation semantic communication framework that integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and channel-aware vector compression. To overcome limitations of prior approaches, including inflexible compression, static retrieval, and the inability to control semantic-cost trade-offs, SAGEComm adopts a principled, modular design. A hybrid optimization algorithm jointly adjusts the semantic quantizer, channel-aware retrieval module, and generative decoder, enabling robust adaptation to dynamic channel conditions (e.g., signal-to-noise ratio, delay, fading) while preserving semantic fidelity. Furthermore, a Semantic Transmission Cost (STC) metric is introduced to evaluate the tradeoffs among semantic fidelity, compression efficiency, and total processing latency. Simulation results indicate that SAGEComm consistently surpasses existing semantic communication systems in semantic fidelity, compression performance, and end-to-end latency. This work underscores the critical role of integrating deep language understanding with physical-layer adaptation to achieve reliable, efficient, and intelligent communications for 6G vehicular networks and beyond.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3676671", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7140088702", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["semantic communication", "Semantic Transmission"]}
{"title": "SComCP: Task-Oriented Semantic Communication for Collaborative Perception", "authors": ["Jipeng Gan", "Yingkun Sheng", "Hua Zhang", "Le Liang", "Hao Ye", "Chongtao Guo", "Shi Jin"], "year": 2026, "publication_date": "2026-01-16", "doi": "10.1109/tvt.2026.3654968", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "13080-13094", "abstract": "Reliable detection of surrounding objects is critical for the safe operation of connected automated vehicles (CAVs). However, inherent limitations such as the restricted perception range and occlusion effects compromise the reliability of singlevehicle perception systems in complex traffic environments. Collaborative perception has emerged as a promising approach by fusing sensor data from surrounding CAVs with diverse viewpoints, thereby improving environmental awareness. Although collaborative perception holds great promise, its performance is bottlenecked by wireless communication constraints, as unreliable and bandwidth-limited channels hinder the transmission of sensor data necessary for real-time perception. To address these challenges, this paper proposes SComCP, a novel taskoriented semantic communication framework for collaborative perception. Specifically, SComCP integrates an importance-aware feature selection network that selects and transmits semantic features most relevant to the perception task, significantly reducing communication overhead without sacrificing accuracy. Furthermore, we design a semantic codec network based on a joint source and channel coding (JSCC) architecture, which enables bidirectional transformation between semantic features and noise-tolerant channel symbols, thereby ensuring stable perception under adverse wireless conditions. Extensive experiments demonstrate the effectiveness of the proposed framework. In particular, compared to existing approaches, SComCP can maintain superior perception performance across various channel conditions, especially in low signal-to-noise ratio (SNR) scenarios. In addition, SComCP exhibits strong generalization capability, enabling the framework to maintain high performance across diverse channel conditions, even when trained with a specific channel model.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3654968", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7124429398", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2507.00895", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["JSCC", "semantic communication", "Semantic Communication"]}
{"title": "Semantic Communication for High-Speed Mobility Scenarios: A Semantic-OTFS Scheme", "authors": ["Yucheng Liu", "Chen Dong", "Haotai Liang", "Weizhi Li", "Xiaodong Xu", "Ping Zhang"], "year": 2026, "publication_date": "2026-03-06", "doi": "10.1109/tvt.2026.3670829", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "8", "pages": "16764-16778", "abstract": "Semantic communication (SemCom), as a novel communication paradigm, has attracted considerable attention due to its superior noise resilience and reduced data transmission requirements. However, existing research on SemCom lacks investigations into high-speed mobility scenarios, which may limit its practical application and development. To explore the prospect of SemCom under high-speed mobility scenarios, this paper proposes an orthogonal time-frequency space (OTFS) based SemCom system named OTFSSC. The proposed system ensures efficient image recovery by leveraging the powerful denoising and generative capabilities of a diffusion model. In addition, the OTFSSC contains signal-to-noise ratio (SNR) and compression ratio (CR) adaptive modules, which can adaptively adjust various SNRs and CRs. Moreover, we propose a semantic OTFS multiple access scheme called Semi-OTFS-MA to efficiently transmit high-speed users and low-speed users. Specifically, Semi-OTFS-MA utilizes deep learning techniques to extract semantic information from both high-speed and low-speed users, and subsequently considers interference suppression properties in the semantic domain so that multiple users can be transmitted simultaneously over the same time-frequency resources. Based on the semantic transmission rate concept, we theoretically derive the theoretical rate domain and power domain corresponding to the proposed Semi-OTFS-MA. Simulation results demonstrate that the proposed scheme achieves superior transmission performance in high-speed mobility scenarios, outperforming traditional communication schemes and other SemCom schemes.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3670829", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "semantic-aware vehicular network", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7134117670", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic communication", "Semantic Communication", "semantic transmission"]}
{"title": "Space-Time Deep Joint Source-Channel Coding for Wireless Image Transmission with Imperfect CSI", "authors": ["Xiaojuan Bai", "Guoqiang Zhao", "Qi Wang", "Xiaobo Yuan"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3717753", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-10", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3717753", "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", "image transmission vehicular network", "video transmission vehicular network", "joint source channel coding image transmission", "joint source channel coding video transmission"], "openalex_id": "https://openalex.org/W7171493603", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Deep Joint Source-Channel", "Wireless Image Transmission"]}
{"title": "The Search for Relevance: A Context-Aware Paradigm Shift in Semantic and Task-Oriented V2X Communications", "authors": ["Luca Lusvarghi", "Javier Gozálvez", "Baldomero Coll-Perales", "Mohammad Irfan Khan", "Miguel Sepulcre", "Seyhan Uçar", "Onur Altıntaş"], "year": 2026, "publication_date": "2026-04-29", "doi": "10.1109/tvt.2026.3688820", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "9", "pages": "20335-20348", "abstract": "The design of communication systems has traditionally prioritized the reliable and timely delivery of data. However, the scalability challenges faced by the evolution towards a data-driven hyper-connected society and economy demand new communication paradigms that carefully curate the content being transmitted. This paper proposes a joint semantic and task-oriented communication paradigm where connected devices transmit only the information necessary to convey the desired meaning that is relevant to the intended receivers, based on their context. We qualitatively and quantitatively analyze the potential benefits of the proposed semantic and task-oriented communication paradigm in the Vehicle-to-Everything (V2X) domain. The V2X domain offers a unique environment for the development and deployment of semantic and task-oriented V2X communications, as it is rich in contextual information and Connected and Autonomous Vehicles (CAVs) are native semantic devices. The qualitative analysis focuses on a cooperative perception use case and shows how semantic and task-oriented V2X communications can reduce the amount of information transmitted by each vehicle without compromising the situational awareness of its intended receivers. The quantitative analysis numerically demonstrates that semantic and task-oriented V2X communications can achieve a two-fold improvement in communication efficiency which can significantly benefit the scalability of future V2X networks.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3688820", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W4415194372", "open_access": {"is_oa": true, "oa_status": "hybrid", "oa_url": "https://doi.org/10.1109/tvt.2026.3688820", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["task-oriented communication"]}
{"title": "TokenComSR: Task-Sensitivity-Guided Token Communication for Wireless Image Super-Resolution", "authors": ["Ye Wang", "Li Qiao", "Zhen Gao", "Hua Wang"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3741365", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "For resource-constrained wireless edge devices over bandwidth-limited fading channels, wireless image transmission using traditional separate coding suffers from the cliff-effect collapse. Prevailing deep joint source-channel coding (JSCC) based on convolutional neural networks can mitigate this issue but usually fail to preserve patch-level structures, thereby preventing adaptive per-token power allocation and limiting token-domain compensation for super-resolution (SR). To address these challenges, we propose a token communication framework with SR (TokenComSR). Specifically, we conceive a task-sensitive power allocation (TSPA) module and a signal-to-noise ratio (SNR)-conditioned token refinement module (TRM). TSPA distills training estimates of task sensitivity into inference token power weights, while TRM estimates an SNR-conditioned residual to correct channel-induced distortion in the token domain before decoding. Building on TSPA and TRM, the proposed TokenComSR pairs a Swin Transformer-based token transceiver with a receiver-side SR module for resource-constrained wireless image transmission. Simulation results confirm the effectiveness of the proposed TSPA and TRM, demonstrating improvements over separate coding and JSCC-SR baselines in both reconstruction fidelity and perceptual quality.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3741365", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7208754059", "open_access": {"is_oa": true, "oa_status": "green", "oa_url": "https://arxiv.org/pdf/2609.03735", "any_repository_has_fulltext": true}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "medium", "screening_reason": "abstract or metadata contains an in-scope semantic/JSCC transmission mechanism; requires full-text screening", "matched_topic_terms": ["deep joint source-channel", "JSCC", "wireless image transmission"]}
{"title": "World Knowledge Enhanced Task-Oriented Semantic Communication Based on Large Language Model", "authors": ["Yuyuan Zhang", "Haitao Zhao", "Yichi Zhang", "Li Zhou", "Jiewen Deng", "Xinfeng Deng", "Jibo Wei"], "year": 2026, "publication_date": "2026-01-01", "doi": "10.1109/tvt.2026.3737391", "venue": "IEEE Transactions on Vehicular Technology", "volume": "", "issue": "", "pages": "1-6", "abstract": "", "publisher_url": "https://doi.org/10.1109/tvt.2026.3737391", "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", "vehicular semantic communication", "V2X semantic communication", "semantic communication autonomous driving", "task-oriented vehicular communication", "goal-oriented vehicular communication", "semantic HARQ vehicular"], "openalex_id": "https://openalex.org/W7221096028", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}
{"title": "ZeroSC-SR: Importance-Aware Zero-Shot Semantic Speech Communication Over Fading Channels", "authors": ["Zewei Wang", "Guanchong Niu", "Yao Tang", "Tat Ming Lok", "Man-On Pun"], "year": 2026, "publication_date": "2026-02-26", "doi": "10.1109/tvt.2026.3666901", "venue": "IEEE Transactions on Vehicular Technology", "volume": "75", "issue": "7", "pages": "15039-15054", "abstract": "This paper introduces an innovative Zero-Shot Semantic Communication System with Speech Reconstruction (ZeroSC-SR), designed to enhance speech transmission and reconstruction in frequency-selective fading channels while remaining compatible with standard digital coding and modulation. By efficiently transmitting phoneme sequences and compressed prompt acoustic features as discrete indices, ZeroSC-SR reduces transmitted data size and achieves a high compression ratio, resulting in lower Character Error Rate (CER) and higher speaker similarity at low transmit power under noisy channels. We quantitatively evaluate the semantic importance of the transmitted phoneme sequences and acoustic features through a task-oriented, mutual-information-based approach. These insights directly guide the development of our Iterative Importance-Aware Allocation and Power Scheduling (IIAPS) strategy, which enhances transmission quality in frequency selective fading channels by allocating subcarriers and power based on semantic importance. Experimental results show that, under fixed power with SNR sweeps, IIAPS outperforms baseline allocation strategies on Word Error Rate (WER) and speaker similarity. Compared to conventional systems, ZeroSC-SR de livers lower CER and higher speaker similarity under varying transmit power, with the greatest improvements occurring at lower power levels.", "publisher_url": "https://doi.org/10.1109/tvt.2026.3666901", "sources": ["OpenAlex full-journal metadata scan"], "matched_queries": ["full-journal metadata scan"], "openalex_id": "https://openalex.org/W7131641836", "open_access": {"is_oa": false, "oa_status": "closed", "oa_url": null, "any_repository_has_fulltext": false}, "oa_pdf_url": "", "record_type": "article", "screening_status": "candidate", "screening_priority": "high", "screening_reason": "title directly names semantic communication/DeepJSCC/task-oriented communication", "matched_topic_terms": ["Semantic Communication"]}