{"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": "excluded", "screening_priority": "medium", "screening_reason": "wireless sensing metric paper without a semantic representation or source-channel transmission chain", "matched_topic_terms": ["joint source-channel coding"]}
{"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": "excluded", "screening_priority": "medium", "screening_reason": "joint neural CSI feedback/precoding paper; it does not study semantic communication or source-channel coding", "matched_topic_terms": ["deep joint source-channel"]}
{"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": "excluded", "screening_priority": "medium", "screening_reason": "over-the-air federated-learning system without a semantic/DeepJSCC transmitted representation", "matched_topic_terms": ["JSCC"]}
{"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": "excluded", "screening_priority": "medium", "screening_reason": "beam-prediction inference paper; semantic scene features are model inputs rather than a designed semantic communication link", "matched_topic_terms": ["semantic-aware network"]}