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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=200" class="pagination-link " aria-label="Page 5" aria-current="page">5 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.20195">arXiv:2503.20195</a> <span> [<a href="https://arxiv.org/pdf/2503.20195">pdf</a>, <a href="https://arxiv.org/format/2503.20195">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Mutual Information-Empowered Task-Oriented Communication: Principles, Applications and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongru Li</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+S">Songjie Xie</a>, <a href="/search/cs?searchtype=author&query=Shao%2C+J">Jiawei Shao</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zixin Wang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.20195v1-abstract-short" style="display: inline;"> Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit task-relevant information for downstream task. This paper provides a comprehensive overview of MI-empowered task-oriented communication, highlighting how MI-based methods can serve as a unifying design framework in vario… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20195v1-abstract-full').style.display = 'inline'; document.getElementById('2503.20195v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.20195v1-abstract-full" style="display: none;"> Mutual information (MI)-based guidelines have recently proven to be effective for designing task-oriented communication systems, where the ultimate goal is to extract and transmit task-relevant information for downstream task. This paper provides a comprehensive overview of MI-empowered task-oriented communication, highlighting how MI-based methods can serve as a unifying design framework in various task-oriented communication scenarios. We begin with the roadmap of MI for designing task-oriented communication systems, and then introduce the roles and applications of MI to guide feature encoding, transmission optimization, and efficient training with two case studies. We further elaborate the limitations and challenges of MI-based methods. Finally, we identify several open issues in MI-based task-oriented communication to inspire future research. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20195v1-abstract-full').style.display = 'none'; document.getElementById('2503.20195v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">8 pages,5 figures, submitted to IEEE for potential publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.14882">arXiv:2503.14882</a> <span> [<a href="https://arxiv.org/pdf/2503.14882">pdf</a>, <a href="https://arxiv.org/format/2503.14882">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Communication-Efficient Distributed On-Device LLM Inference Over Wireless Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+K">Kai Zhang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.14882v1-abstract-short" style="display: inline;"> Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices. To address this issue, we propose a novel distributed on-device LLM inference framework that leverages tensor parallelism to partition the neural network tensors (e.g… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.14882v1-abstract-full').style.display = 'inline'; document.getElementById('2503.14882v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.14882v1-abstract-full" style="display: none;"> Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices. To address this issue, we propose a novel distributed on-device LLM inference framework that leverages tensor parallelism to partition the neural network tensors (e.g., weight matrices) of one LLM across multiple edge devices for collaborative inference. A key challenge in tensor parallelism is the frequent all-reduce operations for aggregating intermediate layer outputs across participating devices, which incurs significant communication overhead. To alleviate this bottleneck, we propose an over-the-air computation (AirComp) approach that harnesses the analog superposition property of wireless multiple-access channels to perform fast all-reduce steps. To utilize the heterogeneous computational capabilities of edge devices and mitigate communication distortions, we investigate a joint model assignment and transceiver optimization problem to minimize the average transmission error. The resulting mixed-timescale stochastic non-convex optimization problem is intractable, and we propose an efficient two-stage algorithm to solve it. Moreover, we prove that the proposed algorithm converges almost surely to a stationary point of the original problem. Comprehensive simulation results will show that the proposed framework outperforms existing benchmark schemes, achieving up to 5x inference speed acceleration and improving inference accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.14882v1-abstract-full').style.display = 'none'; document.getElementById('2503.14882v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2502.12559</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.13940">arXiv:2503.13940</a> <span> [<a href="https://arxiv.org/pdf/2503.13940">pdf</a>, <a href="https://arxiv.org/format/2503.13940">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Multi-Modal Self-Supervised Semantic Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+H">Hang Zhao</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongru Li</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+D">Dongfang Xu</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.13940v1-abstract-short" style="display: inline;"> Semantic communication is emerging as a promising paradigm that focuses on the extraction and transmission of semantic meanings using deep learning techniques. While current research primarily addresses the reduction of semantic communication overhead, it often overlooks the training phase, which can incur significant communication costs in dynamic wireless environments. To address this challenge,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13940v1-abstract-full').style.display = 'inline'; document.getElementById('2503.13940v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.13940v1-abstract-full" style="display: none;"> Semantic communication is emerging as a promising paradigm that focuses on the extraction and transmission of semantic meanings using deep learning techniques. While current research primarily addresses the reduction of semantic communication overhead, it often overlooks the training phase, which can incur significant communication costs in dynamic wireless environments. To address this challenge, we propose a multi-modal semantic communication system that leverages multi-modal self-supervised learning to enhance task-agnostic feature extraction. The proposed approach employs self-supervised learning during the pre-training phase to extract task-agnostic semantic features, followed by supervised fine-tuning for downstream tasks. This dual-phase strategy effectively captures both modality-invariant and modality-specific features while minimizing training-related communication overhead. Experimental results on the NYU Depth V2 dataset demonstrate that the proposed method significantly reduces training-related communication overhead while maintaining or exceeding the performance of existing supervised learning approaches. The findings underscore the advantages of multi-modal self-supervised learning in semantic communication, paving the way for more efficient and scalable edge inference systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.13940v1-abstract-full').style.display = 'none'; document.getElementById('2503.13940v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.12830">arXiv:2503.12830</a> <span> [<a href="https://arxiv.org/pdf/2503.12830">pdf</a>, <a href="https://arxiv.org/ps/2503.12830">ps</a>, <a href="https://arxiv.org/format/2503.12830">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users and Hardware Impairments Over Correlated Rayleigh Fading Channels </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qian%2C+J">Jun Qian</a>, <a href="/search/cs?searchtype=author&query=Murch%2C+R">Ross Murch</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.12830v1-abstract-short" style="display: inline;"> Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and multi-element STAR-RISs, accounting for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.12830v1-abstract-full').style.display = 'inline'; document.getElementById('2503.12830v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.12830v1-abstract-full" style="display: none;"> Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and multi-element STAR-RISs, accounting for transceiver hardware impairments. We first establish the system model of STAR-RIS-assisted cell-free massive MIMO systems with multi-antenna users. Subsequently, we analyze two uplink implementations: local processing and centralized decoding (Level 1), and fully centralized processing (Level 2), both implementations incorporating hardware impairments. We study the local and global minimum mean square error (MMSE) combining schemes to maximize the uplink spectral efficiency (SE) for Level 1 and Level 2, respectively. The MMSE-based successive interference cancellation detector is utilized to compute the uplink SE. We introduce the optimal large-scale fading decoding at the central processing unit and derive closed-form SE expressions utilizing maximum ratio combining at APs for Level 1. Our numerical results reveal that hardware impairments negatively affect SE performance, particularly at the user end. However, this degradation can be mitigated by increasing the number of user antennas. Enhancing the number of APs and STAR-RIS elements also improves performance and mitigates performance degradation. Notably, unlike conventional results based on direct links, our findings show that Level 2 consistently outperforms Level 1 with arbitrary combining schemes for the proposed STAR-RIS-assisted system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.12830v1-abstract-full').style.display = 'none'; document.getElementById('2503.12830v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 6 figures, This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.04040">arXiv:2503.04040</a> <span> [<a href="https://arxiv.org/pdf/2503.04040">pdf</a>, <a href="https://arxiv.org/format/2503.04040">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liao%2C+T">Tianyi Liao</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+W">Wei Guo</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.04040v1-abstract-short" style="display: inline;"> The fluid antenna system (FAS) has emerged as a disruptive technology for future wireless networks, offering unprecedented degrees of freedom (DoF) through the dynamic configuration of antennas in response to propagation environment variations. The integration of fluid antennas (FAs) with multiuser multiple-input multiple-output (MU-MIMO) networks promises substantial weighted sum rate (WSR) gains… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04040v1-abstract-full').style.display = 'inline'; document.getElementById('2503.04040v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.04040v1-abstract-full" style="display: none;"> The fluid antenna system (FAS) has emerged as a disruptive technology for future wireless networks, offering unprecedented degrees of freedom (DoF) through the dynamic configuration of antennas in response to propagation environment variations. The integration of fluid antennas (FAs) with multiuser multiple-input multiple-output (MU-MIMO) networks promises substantial weighted sum rate (WSR) gains via joint beamforming and FA position optimization. However, the joint design is challenging due to the strong coupling between beamforming matrices and antenna positions. To address the challenge, we propose a novel block coordinate ascent (BCA)-based method in FA-assisted MU-MIMO networks. Specifically, we first employ matrix fractional programming techniques to reformulate the original complex problem into a more tractable form. Then, we solve the reformulated problem following the BCA principle, where we develop a low-complexity majorization maximization algorithm capable of optimizing all FA positions simultaneously. To further reduce the computational, storage, and interconnection costs, we propose a decentralized implementation for our proposed algorithm by utilizing the decentralized baseband processing (DBP) architecture. Simulation results demonstrate that with our proposed algorithm, the FA-assisted MU-MIMO system achieves up to a 47% WSR improvement over conventional MIMO networks equipped with fixed-position antennas. Moreover, the decentralized implementation reduces computation time by approximately 70% and has similar performance compared with the centralized implementation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04040v1-abstract-full').style.display = 'none'; document.getElementById('2503.04040v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 6 figures, submitted to an IEEE Journal for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17922">arXiv:2502.17922</a> <span> [<a href="https://arxiv.org/pdf/2502.17922">pdf</a>, <a href="https://arxiv.org/format/2502.17922">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Remote Training in Task-Oriented Communication: Supervised or Self-Supervised with Fine-Tuning? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongru Li</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+H">Hang Zhao</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17922v1-abstract-short" style="display: inline;"> Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems with dynamic connection topologies, tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17922v1-abstract-full').style.display = 'inline'; document.getElementById('2502.17922v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17922v1-abstract-full" style="display: none;"> Task-oriented communication focuses on extracting and transmitting only the information relevant to specific tasks, effectively minimizing communication overhead. Most existing methods prioritize reducing this overhead during inference, often assuming feasible local training or minimal training communication resources. However, in real-world wireless systems with dynamic connection topologies, training models locally for each new connection is impractical, and task-specific information is often unavailable before establishing connections. Therefore, minimizing training overhead and enabling label-free, task-agnostic pre-training before the connection establishment are essential for effective task-oriented communication. In this paper, we tackle these challenges by employing a mutual information maximization approach grounded in self-supervised learning and information-theoretic analysis. We propose an efficient strategy that pre-trains the transmitter in a task-agnostic and label-free manner, followed by joint fine-tuning of both the transmitter and receiver in a task-specific, label-aware manner. Simulation results show that our proposed method reduces training communication overhead to about half that of full-supervised methods using the SGD optimizer, demonstrating significant improvements in training efficiency. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17922v1-abstract-full').style.display = 'none'; document.getElementById('2502.17922v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">accepted by ICC 2025</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.12559">arXiv:2502.12559</a> <span> [<a href="https://arxiv.org/pdf/2502.12559">pdf</a>, <a href="https://arxiv.org/format/2502.12559">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Distributed On-Device LLM Inference With Over-the-Air Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+K">Kai Zhang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.12559v1-abstract-short" style="display: inline;"> Large language models (LLMs) have achieved remarkable success across various artificial intelligence tasks. However, their enormous sizes and computational demands pose significant challenges for the deployment on edge devices. To address this issue, we present a distributed on-device LLM inference framework based on tensor parallelism, which partitions neural network tensors (e.g., weight matrice… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12559v1-abstract-full').style.display = 'inline'; document.getElementById('2502.12559v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.12559v1-abstract-full" style="display: none;"> Large language models (LLMs) have achieved remarkable success across various artificial intelligence tasks. However, their enormous sizes and computational demands pose significant challenges for the deployment on edge devices. To address this issue, we present a distributed on-device LLM inference framework based on tensor parallelism, which partitions neural network tensors (e.g., weight matrices) of LLMs among multiple edge devices for collaborative inference. Nevertheless, tensor parallelism involves frequent all-reduce operations to aggregate intermediate layer outputs across participating devices during inference, resulting in substantial communication overhead. To mitigate this bottleneck, we propose an over-the-air computation method that leverages the analog superposition property of wireless multiple-access channels to facilitate fast all-reduce operations. To minimize the average transmission mean-squared error, we investigate joint model assignment and transceiver optimization, which can be formulated as a mixed-timescale stochastic non-convex optimization problem. Then, we develop a mixed-timescale algorithm leveraging semidefinite relaxation and stochastic successive convex approximation methods. Comprehensive simulation results will show that the proposed approach significantly reduces inference latency while improving accuracy. This makes distributed on-device LLM inference practical for resource-constrained edge devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.12559v1-abstract-full').style.display = 'none'; document.getElementById('2502.12559v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.12656">arXiv:2501.12656</a> <span> [<a href="https://arxiv.org/pdf/2501.12656">pdf</a>, <a href="https://arxiv.org/format/2501.12656">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Ji%2C+M">Maoxin Ji</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+K">Kezhi Wang</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.12656v1-abstract-short" style="display: inline;"> On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12656v1-abstract-full').style.display = 'inline'; document.getElementById('2501.12656v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.12656v1-abstract-full" style="display: none;"> On-ramp merging presents a critical challenge in autonomous driving, as vehicles from merging lanes need to dynamically adjust their positions and speeds while monitoring traffic on the main road to prevent collisions. To address this challenge, we propose a novel merging control scheme based on reinforcement learning, which integrates lateral control mechanisms. This approach ensures the smooth integration of vehicles from the merging lane onto the main road, optimizing both fuel efficiency and passenger comfort. Furthermore, we recognize the impact of vehicle-to-vehicle (V2V) communication on control strategies and introduce an enhanced protocol leveraging Cellular Vehicle-to-Everything (C-V2X) Mode 4. This protocol aims to reduce the Age of Information (AoI) and improve communication reliability. In our simulations, we employ two AoI-based metrics to rigorously assess the protocol's effectiveness in autonomous driving scenarios. By combining the NS3 network simulator with Python, we simulate V2V communication and vehicle control simultaneously. The results demonstrate that the enhanced C-V2X Mode 4 outperforms the standard version, while the proposed control scheme ensures safe and reliable vehicle operation during on-ramp merging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.12656v1-abstract-full').style.display = 'none'; document.getElementById('2501.12656v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/PPO-Based-Vehicle-Control-for-Ramp-Merging-Scheme-Assisted-by-Enhanced-C-V2X</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.09839">arXiv:2412.09839</a> <span> [<a href="https://arxiv.org/pdf/2412.09839">pdf</a>, <a href="https://arxiv.org/format/2412.09839">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> AI and Deep Learning for THz Ultra-Massive MIMO: From Model-Driven Approaches to Foundation Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wentao Yu</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Dai%2C+L">Linglong Dai</a>, <a href="/search/cs?searchtype=author&query=Zheng%2C+L">Lizhong Zheng</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.09839v1-abstract-short" style="display: inline;"> In this paper, we explore the potential of artificial intelligence (AI) to address the challenges posed by terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We begin by outlining the characteristics of THz UM-MIMO systems, and identify three primary challenges for the transceiver design: 'hard to compute', 'hard to model', and 'hard to measure'. We argue that AI can pro… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.09839v1-abstract-full').style.display = 'inline'; document.getElementById('2412.09839v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.09839v1-abstract-full" style="display: none;"> In this paper, we explore the potential of artificial intelligence (AI) to address the challenges posed by terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) systems. We begin by outlining the characteristics of THz UM-MIMO systems, and identify three primary challenges for the transceiver design: 'hard to compute', 'hard to model', and 'hard to measure'. We argue that AI can provide a promising solution to these challenges. We then propose two systematic research roadmaps for developing AI algorithms tailored for THz UM-MIMO systems. The first roadmap, called model-driven deep learning (DL), emphasizes the importance to leverage available domain knowledge and advocates for adopting AI only to enhance the bottleneck modules within an established signal processing or optimization framework. We discuss four essential steps to make it work, including algorithmic frameworks, basis algorithms, loss function design, and neural architecture design. Afterwards, we present a forward-looking vision through the second roadmap, i.e., physical layer foundation models. This approach seeks to unify the design of different transceiver modules by focusing on their common foundation, i.e., the wireless channel. We propose to train a single, compact foundation model to estimate the score function of wireless channels, which can serve as a versatile prior for designing a wide variety of transceiver modules. We will also guide the readers through four essential steps, including general frameworks, conditioning, site-specific adaptation, and the joint design of foundation models and model-driven DL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.09839v1-abstract-full').style.display = 'none'; document.getElementById('2412.09839v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">20 pages, 8 figures, tutorial paper. Physical layer foundation models and model-driven deep learning are presented as two systematic research roadmaps for AI-enabled THz ultra-massive MIMO systems</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.01207">arXiv:2412.01207</a> <span> [<a href="https://arxiv.org/pdf/2412.01207">pdf</a>, <a href="https://arxiv.org/format/2412.01207">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Siamese Machine Unlearning with Knowledge Vaporization and Concentration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+S">Songjie Xie</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.01207v1-abstract-short" style="display: inline;"> In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, existing methods suffer from limitations such as insufficient methodological support, high computational complexity, and significant memory demands. In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01207v1-abstract-full').style.display = 'inline'; document.getElementById('2412.01207v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.01207v1-abstract-full" style="display: none;"> In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, existing methods suffer from limitations such as insufficient methodological support, high computational complexity, and significant memory demands. In this work, we propose the concepts of knowledge vaporization and concentration to selectively erase learned knowledge from specific data points while maintaining representations for the remaining data. Utilizing the Siamese networks, we exemplify the proposed concepts and develop an efficient method for machine unlearning. Our proposed Siamese unlearning method does not require additional memory overhead and full access to the remaining dataset. Extensive experiments conducted across multiple unlearning scenarios showcase the superiority of Siamese unlearning over baseline methods, illustrating its ability to effectively remove knowledge from forgetting data, enhance model utility on remaining data, and reduce susceptibility to membership inference attacks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01207v1-abstract-full').style.display = 'none'; document.getElementById('2412.01207v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.00862">arXiv:2412.00862</a> <span> [<a href="https://arxiv.org/pdf/2412.00862">pdf</a>, <a href="https://arxiv.org/format/2412.00862">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Toward Real-Time Edge AI: Model-Agnostic Task-Oriented Communication with Visual Feature Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+S">Songjie Xie</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.00862v1-abstract-short" style="display: inline;"> Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication bet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00862v1-abstract-full').style.display = 'inline'; document.getElementById('2412.00862v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.00862v1-abstract-full" style="display: none;"> Task-oriented communication presents a promising approach to improve the communication efficiency of edge inference systems by optimizing learning-based modules to extract and transmit relevant task information. However, real-time applications face practical challenges, such as incomplete coverage and potential malfunctions of edge servers. This situation necessitates cross-model communication between different inference systems, enabling edge devices from one service provider to collaborate effectively with edge servers from another. Independent optimization of diverse edge systems often leads to incoherent feature spaces, which hinders the cross-model inference for existing task-oriented communication. To facilitate and achieve effective cross-model task-oriented communication, this study introduces a novel framework that utilizes shared anchor data across diverse systems. This approach addresses the challenge of feature alignment in both server-based and on-device scenarios. In particular, by leveraging the linear invariance of visual features, we propose efficient server-based feature alignment techniques to estimate linear transformations using encoded anchor data features. For on-device alignment, we exploit the angle-preserving nature of visual features and propose to encode relative representations with anchor data to streamline cross-model communication without additional alignment procedures during the inference. The experimental results on computer vision benchmarks demonstrate the superior performance of the proposed feature alignment approaches in cross-model task-oriented communications. The runtime and computation overhead analysis further confirm the effectiveness of the proposed feature alignment approaches in real-time applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.00862v1-abstract-full').style.display = 'none'; document.getElementById('2412.00862v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14030">arXiv:2411.14030</a> <span> [<a href="https://arxiv.org/pdf/2411.14030">pdf</a>, <a href="https://arxiv.org/ps/2411.14030">ps</a>, <a href="https://arxiv.org/format/2411.14030">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Performance Analysis of STAR-RIS-Assisted Cell-Free Massive MIMO Systems with Electromagnetic Interference and Phase Errors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qian%2C+J">Jun Qian</a>, <a href="/search/cs?searchtype=author&query=Murch%2C+R">Ross Murch</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14030v1-abstract-short" style="display: inline;"> Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for the next generation of sixth-generation (6G) networks. A promising configuration for their deployment is within cell-free massive multiple-input multiple-output (MIMO) systems. However, despite the advantages that STAR-RISs could bring, challenges such as electromagnetic interference (EM… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14030v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14030v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14030v1-abstract-full" style="display: none;"> Simultaneous Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) are being explored for the next generation of sixth-generation (6G) networks. A promising configuration for their deployment is within cell-free massive multiple-input multiple-output (MIMO) systems. However, despite the advantages that STAR-RISs could bring, challenges such as electromagnetic interference (EMI) and phase errors may lead to significant performance degradation. In this paper, we investigate the impact of EMI and phase errors on STAR-RIS-assisted cell-free massive MIMO systems and propose techniques to mitigate these effects. We introduce a novel projected gradient descent (GD) algorithm for STAR-RIS coefficient matrix design by minimizing the local channel estimation normalised mean square error. We also derive the closed-form expressions of the uplink and downlink spectral efficiency (SE) to analyze system performance with EMI and phase errors, in which fractional power control methods are applied for performance improvement. The results reveal that the projected GD algorithm can effectively tackle EMI and phase errors to improve estimation accuracy and compensate for performance degradation with nearly $10\%\sim20\%$ SE improvement. Moreover, increasing access points (APs), antennas per AP, and STAR-RIS elements can also improve SE performance. Applying STAR-RIS in the proposed system achieves a larger $25\%$-likely SE than conventional RISs. However, the advantages of employing more STAR-RIS elements are reduced when EMI is severe. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14030v1-abstract-full').style.display = 'none'; document.getElementById('2411.14030v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 6 figures. This work has been submitted to the IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13104">arXiv:2411.13104</a> <span> [<a href="https://arxiv.org/pdf/2411.13104">pdf</a>, <a href="https://arxiv.org/format/2411.13104">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zheng Zhang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13104v1-abstract-short" style="display: inline;"> To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13104v1-abstract-full').style.display = 'inline'; document.getElementById('2411.13104v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13104v1-abstract-full" style="display: none;"> To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Introducing the new metric Age of Information (AoI) provides a more comprehensive evaluation of communication system. Additionally, to ensure service quality, user terminals need to possess high computational capabilities, which may lead to increased energy consumption, necessitating a trade-off between communication energy consumption and effectiveness. Given the complexity and dynamics of communication systems, Deep Reinforcement Learning (DRL) serves as an intelligent learning method capable of learning optimal strategies in dynamic environments. Therefore, this paper analyzes the effects of multi-priority queues and NOMA on AoI in the C-V2X vehicular communication system and proposes an energy consumption and AoI optimization method based on DRL. Finally, through comparative simulations with baseline methods, the proposed approach demonstrates its advances in terms of energy consumption and AoI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13104v1-abstract-full').style.display = 'none'; document.getElementById('2411.13104v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/DRL-Based-Optimization-for-Information-of-Age-and-Energy-Consumption-in-C-V2X-Enabled-IoV</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.07806">arXiv:2411.07806</a> <span> [<a href="https://arxiv.org/pdf/2411.07806">pdf</a>, <a href="https://arxiv.org/format/2411.07806">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+T">Tianqu Kang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zixin Wang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.07806v2-abstract-short" style="display: inline;"> Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on resource-limited devices, combining low-rank adaptation (LoRA) with federated l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07806v2-abstract-full').style.display = 'inline'; document.getElementById('2411.07806v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.07806v2-abstract-full" style="display: none;"> Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on resource-limited devices, combining low-rank adaptation (LoRA) with federated learning enables parameter-efficient fine-tuning. Additionally, the split FedFT architecture partitions an FM between edge devices and a central server, reducing the necessity for complete model deployment on individual devices. However, the risk of privacy eavesdropping attacks in FedFT remains a concern, particularly in sensitive areas such as healthcare and finance. In this paper, we propose a split FedFT framework with differential privacy (DP) over wireless networks, where the inherent wireless channel noise in the uplink transmission is utilized to achieve DP guarantees without adding an extra artificial noise. We shall investigate the impact of the wireless noise on convergence performance of the proposed framework. We will also show that by updating only one of the low-rank matrices in the split FedFT with DP, the proposed method can mitigate the noise amplification effect. Simulation results will demonstrate that the proposed framework achieves higher accuracy under strict privacy budgets compared to baseline methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.07806v2-abstract-full').style.display = 'none'; document.getElementById('2411.07806v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.04672">arXiv:2411.04672</a> <span> [<a href="https://arxiv.org/pdf/2411.04672">pdf</a>, <a href="https://arxiv.org/format/2411.04672">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Semantic-Aware Resource Management for C-V2X Platooning via Multi-Agent Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shao%2C+Z">Zhiyu Shao</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+K">Kezhi Wang</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+Q">Qiang Fan</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.04672v1-abstract-short" style="display: inline;"> This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04672v1-abstract-full').style.display = 'inline'; document.getElementById('2411.04672v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.04672v1-abstract-full" style="display: none;"> This paper presents a semantic-aware multi-modal resource allocation (SAMRA) for multi-task using multi-agent reinforcement learning (MARL), termed SAMRAMARL, utilizing in platoon systems where cellular vehicle-to-everything (C-V2X) communication is employed. The proposed approach leverages the semantic information to optimize the allocation of communication resources. By integrating a distributed multi-agent reinforcement learning (MARL) algorithm, SAMRAMARL enables autonomous decision-making for each vehicle, channel assignment optimization, power allocation, and semantic symbol length based on the contextual importance of the transmitted information. This semantic-awareness ensures that both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications prioritize data that is critical for maintaining safe and efficient platoon operations. The framework also introduces a tailored quality of experience (QoE) metric for semantic communication, aiming to maximize QoE in V2V links while improving the success rate of semantic information transmission (SRS). Extensive simulations has demonstrated that SAMRAMARL outperforms existing methods, achieving significant gains in QoE and communication efficiency in C-V2X platooning scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.04672v1-abstract-full').style.display = 'none'; document.getElementById('2411.04672v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Semantic-Aware-Resource-Management-for-C-V2X-Platooning-via-Multi-Agent-Reinforcement-Learning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01458">arXiv:2411.01458</a> <span> [<a href="https://arxiv.org/pdf/2411.01458">pdf</a>, <a href="https://arxiv.org/format/2411.01458">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> </div> <p class="title is-5 mathjax"> Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+Z">Zhang Liu</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/cs?searchtype=author&query=Hou%2C+X">Xiangwang Hou</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+L">Lianfen Huang</a>, <a href="/search/cs?searchtype=author&query=Hosseinalipour%2C+S">Seyyedali Hosseinalipour</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled Ben Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.01458v1-abstract-short" style="display: inline;"> Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01458v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01458v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01458v1-abstract-full" style="display: none;"> Generative AI (GenAI) has emerged as a transformative technology, enabling customized and personalized AI-generated content (AIGC) services. In this paper, we address challenges of edge-enabled AIGC service provisioning, which remain underexplored in the literature. These services require executing GenAI models with billions of parameters, posing significant obstacles to resource-limited wireless edge. We subsequently introduce the formulation of joint model caching and resource allocation for AIGC services to balance a trade-off between AIGC quality and latency metrics. We obtain mathematical relationships of these metrics with the computational resources required by GenAI models via experimentation. Afterward, we decompose the formulation into a model caching subproblem on a long-timescale and a resource allocation subproblem on a short-timescale. Since the variables to be solved are discrete and continuous, respectively, we leverage a double deep Q-network (DDQN) algorithm to solve the former subproblem and propose a diffusion-based deep deterministic policy gradient (D3PG) algorithm to solve the latter. The proposed D3PG algorithm makes an innovative use of diffusion models as the actor network to determine optimal resource allocation decisions. Consequently, we integrate these two learning methods within the overarching two-timescale deep reinforcement learning (T2DRL) algorithm, the performance of which is studied through comparative numerical simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01458v1-abstract-full').style.display = 'none'; document.getElementById('2411.01458v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 8 figures, 39 references</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.22987">arXiv:2410.22987</a> <span> [<a href="https://arxiv.org/pdf/2410.22987">pdf</a>, <a href="https://arxiv.org/format/2410.22987">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> V2X-Assisted Distributed Computing and Control Framework for Connected and Automated Vehicles under Ramp Merging Scenario </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Chu%2C+J">Jiahou Chu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+K">Kezhi Wang</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.22987v1-abstract-short" style="display: inline;"> This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22987v1-abstract-full').style.display = 'inline'; document.getElementById('2410.22987v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.22987v1-abstract-full" style="display: none;"> This paper investigates distributed computing and cooperative control of connected and automated vehicles (CAVs) in ramp merging scenario under transportation cyber-physical system. Firstly, a centralized cooperative trajectory planning problem is formulated subject to the safely constraints and traffic performance in ramp merging scenario, where the trajectories of all vehicles are jointly optimized. To get rid of the reliance on a central controller and reduce computation time, a distributed solution to this problem implemented among CAVs through Vehicles-to-Everything (V2X) communication is proposed. Unlike existing method, our method can distribute the computational task among CAVs and carry out parallel solving through V2X communication. Then, a multi-vehicles model predictive control (MPC) problem aimed at maximizing system stability and minimizing control input is formulated based on the solution of the first problem subject to strict safety constants and input limits. Due to these complex constraints, this problem becomes high-dimensional, centralized, and non-convex. To solve it in a short time, a decomposition and convex reformulation method, namely distributed cooperative iterative model predictive control (DCIMPC), is proposed. This method leverages the communication capability of CAVs to decompose the problem, making full use of the computational resources on vehicles to achieve fast solutions and distributed control. The two above problems with their corresponding solving methods form the systemic framework of the V2X assisted distributed computing and control. Simulations have been conducted to evaluate the framework's convergence, safety, and solving speed. Additionally, extra experiments are conducted to validate the performance of DCIMPC. The results show that our method can greatly improve computation speed without sacrificing system performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.22987v1-abstract-full').style.display = 'none'; document.getElementById('2410.22987v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/V2X-Assisted-Distributed-Computing-and-Control-Framework-for-Connected-and-Automated-Vehicles.git</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17287">arXiv:2409.17287</a> <span> [<a href="https://arxiv.org/pdf/2409.17287">pdf</a>, <a href="https://arxiv.org/format/2409.17287">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Blockchain-Enabled Variational Information Bottleneck for Data Extraction Based on Mutual Information in Internet of Vehicles </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Cui Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+W">Wenjun Zhang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.17287v1-abstract-short" style="display: inline;"> The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmis… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17287v1-abstract-full').style.display = 'inline'; document.getElementById('2409.17287v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17287v1-abstract-full" style="display: none;"> The Internet of Vehicles (IoV) network can address the issue of limited computing resources and data processing capabilities of individual vehicles, but it also brings the risk of privacy leakage to vehicle users. Applying blockchain technology can establish secure data links within the IoV, solving the problems of insufficient computing resources for each vehicle and the security of data transmission over the network. However, with the development of the IoV, the amount of data interaction between multiple vehicles and between vehicles and base stations, roadside units, etc., is continuously increasing. There is a need to further reduce the interaction volume, and intelligent data compression is key to solving this problem. The VIB technique facilitates the training of encoding and decoding models, substantially diminishing the volume of data that needs to be transmitted. This paper introduces an innovative approach that integrates blockchain with VIB, referred to as BVIB, designed to lighten computational workloads and reinforce the security of the network. We first construct a new network framework by separating the encoding and decoding networks to address the computational burden issue, and then propose a new algorithm to enhance the security of IoV networks. We also discuss the impact of the data extraction rate on system latency to determine the most suitable data extraction rate. An experimental framework combining Python and C++ has been established to substantiate the efficacy of our BVIB approach. Comprehensive simulation studies indicate that the BVIB consistently excels in comparison to alternative foundational methodologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17287v1-abstract-full').style.display = 'none'; document.getElementById('2409.17287v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/BVIB-for-Data-Extraction-Based-on Mutual-Information-in-the-IoV</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.04302">arXiv:2409.04302</a> <span> [<a href="https://arxiv.org/pdf/2409.04302">pdf</a>, <a href="https://arxiv.org/format/2409.04302">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Fast Adaptation for Deep Learning-based Wireless Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+O">Ouya Wang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+S">Shenglong Zhou</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+Z">Zhi Ding</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+S">Shi Jin</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a>, <a href="/search/cs?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.04302v1-abstract-short" style="display: inline;"> The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04302v1-abstract-full').style.display = 'inline'; document.getElementById('2409.04302v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.04302v1-abstract-full" style="display: none;"> The integration with artificial intelligence (AI) is recognized as one of the six usage scenarios in next-generation wireless communications. However, several critical challenges hinder the widespread application of deep learning (DL) techniques in wireless communications. In particular, existing DL-based wireless communications struggle to adapt to the rapidly changing wireless environments. In this paper, we discuss fast adaptation for DL-based wireless communications by using few-shot learning (FSL) techniques. We first identify the differences between fast adaptation in wireless communications and traditional AI tasks by highlighting two distinct FSL design requirements for wireless communications. To establish a wide perspective, we present a comprehensive review of the existing FSL techniques in wireless communications that satisfy these two design requirements. In particular, we emphasize the importance of applying domain knowledge in achieving fast adaptation. We specifically focus on multiuser multiple-input multiple-output (MU-MIMO) precoding as an examples to demonstrate the advantages of the FSL to achieve fast adaptation in wireless communications. Finally, we highlight several open research issues for achieving broadscope future deployment of fast adaptive DL in wireless communication applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.04302v1-abstract-full').style.display = 'none'; document.getElementById('2409.04302v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.14831">arXiv:2408.14831</a> <span> [<a href="https://arxiv.org/pdf/2408.14831">pdf</a>, <a href="https://arxiv.org/format/2408.14831">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gu%2C+X">Xueying Gu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.14831v1-abstract-short" style="display: inline;"> Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. O… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14831v1-abstract-full').style.display = 'inline'; document.getElementById('2408.14831v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.14831v1-abstract-full" style="display: none;"> Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14831v1-abstract-full').style.display = 'none'; document.getElementById('2408.14831v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to Digital Communications and Networks. The source code has been released at: https://github.com/qiongwu86/Federated-SSL-task-offloading-and-resource-allocation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.09194">arXiv:2408.09194</a> <span> [<a href="https://arxiv.org/pdf/2408.09194">pdf</a>, <a href="https://arxiv.org/format/2408.09194">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gu%2C+X">Xueying Gu</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+Q">Qiang Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.09194v1-abstract-short" style="display: inline;"> In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09194v1-abstract-full').style.display = 'inline'; document.getElementById('2408.09194v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.09194v1-abstract-full" style="display: none;"> In the Internet of Vehicles (IoV), Federated Learning (FL) provides a privacy-preserving solution by aggregating local models without sharing data. Traditional supervised learning requires image data with labels, but data labeling involves significant manual effort. Federated Self-Supervised Learning (FSSL) utilizes Self-Supervised Learning (SSL) for local training in FL, eliminating the need for labels while protecting privacy. Compared to other SSL methods, Momentum Contrast (MoCo) reduces the demand for computing resources and storage space by creating a dictionary. However, using MoCo in FSSL requires uploading the local dictionary from vehicles to Base Station (BS), which poses a risk of privacy leakage. Simplified Contrast (SimCo) addresses the privacy leakage issue in MoCo-based FSSL by using dual temperature instead of a dictionary to control sample distribution. Additionally, considering the negative impact of motion blur on model aggregation, and based on SimCo, we propose a motion blur-resistant FSSL method, referred to as BFSSL. Furthermore, we address energy consumption and delay in the BFSSL process by proposing a Deep Reinforcement Learning (DRL)-based resource allocation scheme, called DRL-BFSSL. In this scheme, BS allocates the Central Processing Unit (CPU) frequency and transmission power of vehicles to minimize energy consumption and latency, while aggregating received models based on the motion blur level. Simulation results validate the effectiveness of our proposed aggregation and resource allocation methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.09194v1-abstract-full').style.display = 'none'; document.getElementById('2408.09194v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/DRL-BFSSL</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08074">arXiv:2408.08074</a> <span> [<a href="https://arxiv.org/pdf/2408.08074">pdf</a>, <a href="https://arxiv.org/format/2408.08074">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Survey on Integrated Sensing, Communication, and Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wen%2C+D">Dingzhu Wen</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Y">Yong Zhou</a>, <a href="/search/cs?searchtype=author&query=Li%2C+X">Xiaoyang Li</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+Y">Yuanming Shi</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+K">Kaibin Huang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08074v3-abstract-short" style="display: inline;"> The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08074v3-abstract-full').style.display = 'inline'; document.getElementById('2408.08074v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08074v3-abstract-full" style="display: none;"> The forthcoming generation of wireless technology, 6G, aims to usher in an era of ubiquitous intelligent services, where everything is interconnected and intelligent. This vision requires the seamless integration of three fundamental modules: Sensing for information acquisition, communication for information sharing, and computation for information processing and decision-making. These modules are intricately linked, especially in complex tasks such as edge learning and inference. However, the performance of these modules is interdependent, creating a resource competition for time, energy, and bandwidth. Existing techniques like integrated communication and computation (ICC), integrated sensing and computation (ISC), and integrated sensing and communication (ISAC) have made partial strides in addressing this challenge, but they fall short of meeting the extreme performance requirements. To overcome these limitations, it is essential to develop new techniques that comprehensively integrate sensing, communication, and computation. This integrated approach, known as Integrated Sensing, Communication, and Computation (ISCC), offers a systematic perspective for enhancing task performance. This paper begins with a comprehensive survey of historic and related techniques such as ICC, ISC, and ISAC, highlighting their strengths and limitations. It then discusses the benefits, functions, and challenges of ISCC. Subsequently, the state-of-the-art signal designs for ISCC, along with network resource management strategies specifically tailored for ISCC are explored. Furthermore, this paper discusses the exciting research opportunities that lie ahead for implementing ISCC in future advanced networks, and the unresolved issues requiring further investigation. ISCC is expected to unlock the full potential of intelligent connectivity, paving the way for groundbreaking applications and services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08074v3-abstract-full').style.display = 'none'; document.getElementById('2408.08074v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This version is accepted by IEEE Communications Surveys & Tutorials on Dec. 18, 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.04825">arXiv:2408.04825</a> <span> [<a href="https://arxiv.org/pdf/2408.04825">pdf</a>, <a href="https://arxiv.org/format/2408.04825">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MNET.2024.3421517">10.1109/MNET.2024.3421517 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Towards Effective and Interpretable Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Youlong Wu</a>, <a href="/search/cs?searchtype=author&query=Shi%2C+Y">Yuanmin Shi</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+S">Shuai Ma</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+C">Chunxiao Jiang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+W">Wei Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.04825v1-abstract-short" style="display: inline;"> With the exponential surge in traffic data and the pressing need for ultra-low latency in emerging intelligence applications, it is envisioned that 6G networks will demand disruptive communication technologies to foster ubiquitous intelligence and succinctness within the human society. Semantic communication, a novel paradigm, holds the promise of significantly curtailing communication overhead an… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04825v1-abstract-full').style.display = 'inline'; document.getElementById('2408.04825v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.04825v1-abstract-full" style="display: none;"> With the exponential surge in traffic data and the pressing need for ultra-low latency in emerging intelligence applications, it is envisioned that 6G networks will demand disruptive communication technologies to foster ubiquitous intelligence and succinctness within the human society. Semantic communication, a novel paradigm, holds the promise of significantly curtailing communication overhead and latency by transmitting only task-relevant information. Despite numerous efforts in both theoretical frameworks and practical implementations of semantic communications, a substantial theory-practice gap complicates the theoretical analysis and interpretation, particularly when employing black-box machine learning techniques. This article initially delves into information-theoretic metrics such as semantic entropy, semantic distortions, and semantic communication rate to characterize the information flow in semantic communications. Subsequently, it provides a guideline for implementing semantic communications to ensure both theoretical interpretability and communication effectiveness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.04825v1-abstract-full').style.display = 'none'; document.getElementById('2408.04825v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by IEEE Network Magazine</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.20840">arXiv:2407.20840</a> <span> [<a href="https://arxiv.org/pdf/2407.20840">pdf</a>, <a href="https://arxiv.org/format/2407.20840">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Large Language Model (LLM)-enabled Graphs in Dynamic Networking </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+G">Geng Sun</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yixian Wang</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiacheng Wang</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+X">Xinying Wang</a>, <a href="/search/cs?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.20840v1-abstract-short" style="display: inline;"> Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20840v1-abstract-full').style.display = 'inline'; document.getElementById('2407.20840v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.20840v1-abstract-full" style="display: none;"> Recent advances in generative artificial intelligence (AI), and particularly the integration of large language models (LLMs), have had considerable impact on multiple domains. Meanwhile, enhancing dynamic network performance is a crucial element in promoting technological advancement and meeting the growing demands of users in many applications areas involving networks. In this article, we explore an integration of LLMs and graphs in dynamic networks, focusing on potential applications and a practical study. Specifically, we first review essential technologies and applications of LLM-enabled graphs, followed by an exploration of their advantages in dynamic networking. Subsequently, we introduce and analyze LLM-enabled graphs and their applications in dynamic networks from the perspective of LLMs as different roles. On this basis, we propose a novel framework of LLM-enabled graphs for networking optimization, and then present a case study on UAV networking, concentrating on optimizing UAV trajectory and communication resource allocation to validate the effectiveness of the proposed framework. Finally, we outline several potential future extensions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20840v1-abstract-full').style.display = 'none'; document.getElementById('2407.20840v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 6 figures, published to IEEE NETWORK</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.13123">arXiv:2407.13123</a> <span> [<a href="https://arxiv.org/pdf/2407.13123">pdf</a>, <a href="https://arxiv.org/format/2407.13123">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qi%2C+K">Kangwei Qi</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.13123v1-abstract-short" style="display: inline;"> Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challen… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13123v1-abstract-full').style.display = 'inline'; document.getElementById('2407.13123v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.13123v1-abstract-full" style="display: none;"> Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of Reconfigurable Intelligent Surfaces (RIS), which provide alternative communication pathways to assist vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider a RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient (TD3) and some typical stochastic schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.13123v1-abstract-full').style.display = 'none'; document.getElementById('2407.13123v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at https://github.com/qiongwu86/DDPG-RIS-MADDPG-POWER. arXiv admin note: text overlap with arXiv:2406.11318</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.10386">arXiv:2407.10386</a> <span> [<a href="https://arxiv.org/pdf/2407.10386">pdf</a>, <a href="https://arxiv.org/ps/2407.10386">ps</a>, <a href="https://arxiv.org/format/2407.10386">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MeditCom61057.2024.10621117">10.1109/MeditCom61057.2024.10621117 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Two-Phase Channel Estimation for RIS-Aided Cell-Free Massive MIMO with Electromagnetic Interference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qian%2C+J">Jun Qian</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Chi Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a>, <a href="/search/cs?searchtype=author&query=Murch%2C+R">Ross Murch</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.10386v1-abstract-short" style="display: inline;"> This work considers a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system with RIS spatial correlation and electromagnetic interference (EMI). We propose a two-phase channel estimation scheme with fractional power control-aided pilot assignment to improve the estimation accuracy and system performance of RIS-aided cell-free massive MIMO sys… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10386v1-abstract-full').style.display = 'inline'; document.getElementById('2407.10386v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.10386v1-abstract-full" style="display: none;"> This work considers a reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (MIMO) system with RIS spatial correlation and electromagnetic interference (EMI). We propose a two-phase channel estimation scheme with fractional power control-aided pilot assignment to improve the estimation accuracy and system performance of RIS-aided cell-free massive MIMO systems. Additionally, we derive the closed-form expressions of the downlink spectral efficiency (SE) with conjugate beamforming to evaluate the impact of EMI among RIS elements on the system performance. Numerical results validate that the proposed two-phase scheme can compensate for the performance degradation caused by EMI in terms of estimation accuracy and downlink SE. Moreover, the benefits of introducing RISs and increasing access points (APs) are illustrated. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10386v1-abstract-full').style.display = 'none'; document.getElementById('2407.10386v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 3 figures. This paper has been submitted to 2024 IEEE MeditCom</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.08462">arXiv:2407.08462</a> <span> [<a href="https://arxiv.org/pdf/2407.08462">pdf</a>, <a href="https://arxiv.org/format/2407.08462">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Cui Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+W">Wenjun Zhang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+Q">Qiang Fan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.08462v1-abstract-short" style="display: inline;"> Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient q… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08462v1-abstract-full').style.display = 'inline'; document.getElementById('2407.08462v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.08462v1-abstract-full" style="display: none;"> Federated Learning (FL) can protect the privacy of the vehicles in vehicle edge computing (VEC) to a certain extent through sharing the gradients of vehicles' local models instead of local data. The gradients of vehicles' local models are usually large for the vehicular artificial intelligence (AI) applications, thus transmitting such large gradients would cause large per-round latency. Gradient quantization has been proposed as one effective approach to reduce the per-round latency in FL enabled VEC through compressing gradients and reducing the number of bits, i.e., the quantization level, to transmit gradients. The selection of quantization level and thresholds determines the quantization error, which further affects the model accuracy and training time. To do so, the total training time and quantization error (QE) become two key metrics for the FL enabled VEC. It is critical to jointly optimize the total training time and QE for the FL enabled VEC. However, the time-varying channel condition causes more challenges to solve this problem. In this paper, we propose a distributed deep reinforcement learning (DRL)-based quantization level allocation scheme to optimize the long-term reward in terms of the total training time and QE. Extensive simulations identify the optimal weighted factors between the total training time and QE, and demonstrate the feasibility and effectiveness of the proposed scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.08462v1-abstract-full').style.display = 'none'; document.getElementById('2407.08462v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Distributed-Deep-Reinforcement-Learning-Based-Gradient Quantization-for-Federated-Learning-Enabled-Vehicle-Edge-Computing</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.07575">arXiv:2407.07575</a> <span> [<a href="https://arxiv.org/pdf/2407.07575">pdf</a>, <a href="https://arxiv.org/format/2407.07575">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+Y">Yu Xie</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.07575v1-abstract-short" style="display: inline;"> As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtua… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07575v1-abstract-full').style.display = 'inline'; document.getElementById('2407.07575v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.07575v1-abstract-full" style="display: none;"> As a promising technology, vehicular edge computing (VEC) can provide computing and caching services by deploying VEC servers near vehicles. However, VEC networks still face challenges such as high vehicle mobility. Digital twin (DT), an emerging technology, can predict, estimate, and analyze real-time states by digitally modeling objects in the physical world. By integrating DT with VEC, a virtual vehicle DT can be created in the VEC server to monitor the real-time operating status of vehicles. However, maintaining the vehicle DT model requires ongoing attention from the VEC server, which also needs to offer computing services for the vehicles. Therefore, effective allocation and scheduling of VEC server resources are crucial. This study focuses on a general VEC network with a single VEC service and multiple vehicles, examining the two types of delays caused by twin maintenance and computational processing within the network. By transforming the problem using satisfaction functions, we propose an optimization problem aimed at maximizing each vehicle's resource utility to determine the optimal resource allocation strategy. Given the non-convex nature of the issue, we employ multi-agent Markov decision processes to reformulate the problem. Subsequently, we propose the twin maintenance and computing task processing resource collaborative scheduling (MADRL-CSTC) algorithm, which leverages multi-agent deep reinforcement learning. Through experimental comparisons with alternative algorithms, it demonstrates that our proposed approach is effective in terms of resource allocation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07575v1-abstract-full').style.display = 'none'; document.getElementById('2407.07575v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at:https://github.com/qiongwu86/Resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.06518">arXiv:2407.06518</a> <span> [<a href="https://arxiv.org/pdf/2407.06518">pdf</a>, <a href="https://arxiv.org/format/2407.06518">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ji%2C+M">Maoxin Ji</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.06518v1-abstract-short" style="display: inline;"> In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06518v1-abstract-full').style.display = 'inline'; document.getElementById('2407.06518v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.06518v1-abstract-full" style="display: none;"> In the rapidly evolving landscape of Internet of Vehicles (IoV) technology, Cellular Vehicle-to-Everything (C-V2X) communication has attracted much attention due to its superior performance in coverage, latency, and throughput. Resource allocation within C-V2X is crucial for ensuring the transmission of safety information and meeting the stringent requirements for ultra-low latency and high reliability in Vehicle-to-Vehicle (V2V) communication. This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes and employing the Graph Sample and Aggregation (GraphSAGE) model to adapt to changes in graph structure, the model aims to ensure a high success rate for V2V communication while minimizing interference on Vehicle-to-Infrastructure (V2I) links, thereby ensuring the successful transmission of V2V link information and maintaining high transmission rates for V2I links. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment, allowing vehicles to extract low-dimensional features that include structural information from the graph network based on local observations and to make independent resource allocation decisions. Simulation results indicate that the introduction of GNN, with a modest increase in computational load, effectively enhances the decision-making quality of agents, demonstrating superiority to other methods. This study not only provides a theoretically efficient resource allocation strategy for V2V and V2I communications but also paves a new technical path for resource management in practical IoV environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.06518v1-abstract-full').style.display = 'none'; document.getElementById('2407.06518v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 11 figures. This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/GNN-and-DRL-Based-Resource-Allocation-for-V2X-Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.03785">arXiv:2407.03785</a> <span> [<a href="https://arxiv.org/pdf/2407.03785">pdf</a>, <a href="https://arxiv.org/ps/2407.03785">ps</a>, <a href="https://arxiv.org/format/2407.03785">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Impact of Channel Aging and Electromagnetic Interference on RIS-Assisted Cell-Free Massive MIMO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qian%2C+J">Jun Qian</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+C">Chi Zhang</a>, <a href="/search/cs?searchtype=author&query=Murch%2C+R">Ross Murch</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.03785v2-abstract-short" style="display: inline;"> Cell-free massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RISs) are two potential sixth-generation (6G) technologies. However, channel aging due to user mobility and electromagnetic interference (EMI) impinging on RISs can negatively affect performance. Existing research on RIS-assisted cell-free massive MIMO systems often overlooks these issues. This work fo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03785v2-abstract-full').style.display = 'inline'; document.getElementById('2407.03785v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.03785v2-abstract-full" style="display: none;"> Cell-free massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RISs) are two potential sixth-generation (6G) technologies. However, channel aging due to user mobility and electromagnetic interference (EMI) impinging on RISs can negatively affect performance. Existing research on RIS-assisted cell-free massive MIMO systems often overlooks these issues. This work focuses on the impact and mitigation of channel aging and EMI on RIS-assisted cell-free massive MIMO systems over spatially correlated channels. To mitigate the degradation caused by these issues, we introduce a novel two-phase channel estimation scheme with large-scale fading coefficient-aided pilot assignment to enhance channel estimation accuracy compared to conventional minimum mean square error estimators. We then develop closed-form expressions for the downlink spectral efficiency (SE) performance and using these, optimize the sum downlink SE with respect to the RIS coefficient matrices. This optimization is accomplished by the projected gradient ascent (GA) algorithm. The results show that our proposed two-phase channel estimation scheme can achieve a nearly 10%-likely SE improvement compared to conventional channel estimation in environments affected by channel aging. A further 10%~15%-likely SE improvement is achieved using the proposed GA algorithm compared to random RIS phases, especially when the number of RISs increases. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.03785v2-abstract-full').style.display = 'none'; document.getElementById('2407.03785v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper contains 13 pages and 7 figures. This paper has been submitted to IEEE Journal for potential publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.02342">arXiv:2407.02342</a> <span> [<a href="https://arxiv.org/pdf/2407.02342">pdf</a>, <a href="https://arxiv.org/ps/2407.02342">ps</a>, <a href="https://arxiv.org/format/2407.02342">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+W">Wenhua Wang</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.02342v1-abstract-short" style="display: inline;"> With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RS… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.02342v1-abstract-full').style.display = 'inline'; document.getElementById('2407.02342v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.02342v1-abstract-full" style="display: none;"> With the rapid development of intelligent vehicles and Intelligent Transport Systems (ITS), the sensors such as cameras and LiDAR installed on intelligent vehicles provides higher capacity of executing computation-intensive and delay-sensitive tasks, thereby raising deployment costs. To address this issue, Vehicular Edge Computing (VEC) has been proposed to process data through Road Side Units (RSUs) to support real-time applications. This paper focuses on the Age of Information (AoI) as a key metric for data freshness and explores task offloading issues for vehicles under RSU communication resource constraints. We adopt a Multi-agent Deep Reinforcement Learning (MADRL) approach, allowing vehicles to autonomously make optimal data offloading decisions. However, MADRL poses risks of vehicle information leakage during communication learning and centralized training. To mitigate this, we employ a Federated Learning (FL) framework that shares model parameters instead of raw data to protect the privacy of vehicle users. Building on this, we propose an innovative distributed federated learning framework combining Graph Neural Networks (GNN), named Federated Graph Neural Network Multi-Agent Reinforcement Learning (FGNN-MADRL), to optimize AoI across the system. For the first time, road scenarios are constructed as graph data structures, and a GNN-based federated learning framework is proposed, effectively combining distributed and centralized federated aggregation. Furthermore, we propose a new MADRL algorithm that simplifies decision making and enhances offloading efficiency, further reducing the decision complexity. Simulation results demonstrate the superiority of our proposed approach to other methods through simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.02342v1-abstract-full').style.display = 'none'; document.getElementById('2407.02342v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Optimizing-AoI-in-VEC-with-Federated-Graph-Neural-Network-Multi-Agent-Reinforcement-Learning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11245">arXiv:2406.11245</a> <span> [<a href="https://arxiv.org/pdf/2406.11245">pdf</a>, <a href="https://arxiv.org/format/2406.11245">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Qi%2C+K">Kangwei Qi</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.11245v1-abstract-short" style="display: inline;"> Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11245v1-abstract-full').style.display = 'inline'; document.getElementById('2406.11245v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11245v1-abstract-full" style="display: none;"> Reconfigurable Intelligent Surface (RIS) is a pivotal technology in communication, offering an alternative path that significantly enhances the link quality in wireless communication environments. In this paper, we propose a RIS-assisted internet of vehicles (IoV) network, considering the vehicle-to-everything (V2X) communication method. In addition, in order to improve the timeliness of vehicle-to-infrastructure (V2I) links and the stability of vehicle-to-vehicle (V2V) links, we introduce the age of information (AoI) model and the payload transmission probability model. Therefore, with the objective of minimizing the AoI of V2I links and prioritizing transmission of V2V links payload, we construct this optimization problem as an Markov decision process (MDP) problem in which the BS serves as an agent to allocate resources and control phase-shift for the vehicles using the soft actor-critic (SAC) algorithm, which gradually converges and maintains a high stability. A AoI-aware joint vehicular resource allocation and RIS phase-shift control scheme based on SAC algorithm is proposed and simulation results show that its convergence speed, cumulative reward, AoI performance, and payload transmission probability outperforms those of proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), twin delayed deep deterministic policy gradient (TD3) and stochastic algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11245v1-abstract-full').style.display = 'none'; document.getElementById('2406.11245v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at https://github.com/qiongwu86/RIS-RB-AoI-V2X-DRL.git</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07992">arXiv:2406.07992</a> <span> [<a href="https://arxiv.org/pdf/2406.07992">pdf</a>, <a href="https://arxiv.org/format/2406.07992">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Federated Online Restless Bandit Framework for Cooperative Resource Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tong%2C+J">Jingwen Tong</a>, <a href="/search/cs?searchtype=author&query=Li%2C+X">Xinran Li</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+L">Liqun Fu</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07992v1-abstract-short" style="display: inline;"> Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07992v1-abstract-full').style.display = 'inline'; document.getElementById('2406.07992v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07992v1-abstract-full" style="display: none;"> Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we study the cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of $\mathcal{O}(\sqrt{T\log(T)})$ and better performance compared with baselines. More importantly, its sample complexity decreases with the number of agents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07992v1-abstract-full').style.display = 'none'; document.getElementById('2406.07992v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07213">arXiv:2406.07213</a> <span> [<a href="https://arxiv.org/pdf/2406.07213">pdf</a>, <a href="https://arxiv.org/format/2406.07213">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Semantic-Aware Spectrum Sharing in Internet of Vehicles Based on Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shao%2C+Z">Zhiyu Shao</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Cheng%2C+N">Nan Cheng</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wen Chen</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiangzhou Wang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07213v3-abstract-short" style="display: inline;"> This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement le… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07213v3-abstract-full').style.display = 'inline'; document.getElementById('2406.07213v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07213v3-abstract-full" style="display: none;"> This work aims to investigate semantic communication in high-speed mobile Internet of vehicles (IoV) environments, with a focus on the spectrum sharing between vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. We specifically address spectrum scarcity and network traffic and then propose a semantic-aware spectrum sharing algorithm (SSS) based on the deep reinforcement learning (DRL) soft actor-critic (SAC) approach. Firstly, we delve into the extraction of semantic information. Secondly, we redefine metrics for semantic information in V2V and V2I spectrum sharing in IoV environments, introducing high-speed semantic spectrum efficiency (HSSE) and semantic transmission rate (HSR). Finally, we employ the SAC algorithm for decision optimization in V2V and V2I spectrum sharing based on semantic information. This optimization encompasses the optimal link of V2V and V2I sharing strategies, the transmission power for vehicles sending semantic information and the length of transmitted semantic symbols, aiming at maximizing HSSE of V2I and enhancing success rate of effective semantic information transmission (SRS) of V2V. Experimental results demonstrate that the SSS algorithm outperforms other baseline algorithms, including other traditional-communication-based spectrum sharing algorithms and spectrum sharing algorithm using other reinforcement learning approaches. The SSS algorithm exhibits a 15% increase in HSSE and approximately a 7% increase in SRS. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07213v3-abstract-full').style.display = 'none'; document.getElementById('2406.07213v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE Journal. The source code has been released at: https://github.com/qiongwu86/Semantic-Aware-Spectrum-Sharing-in-Internet-of-Vehicles-Based-on-Deep-Reinforcement-Learning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.10096">arXiv:2405.10096</a> <span> [<a href="https://arxiv.org/pdf/2405.10096">pdf</a>, <a href="https://arxiv.org/format/2405.10096">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/MeditCom61057.2024.10621075">10.1109/MeditCom61057.2024.10621075 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> The Effect of Quantization in Federated Learning: A R茅nyi Differential Privacy Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kang%2C+T">Tianqu Kang</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+L">Lumin Liu</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S+H">S. H. Song</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.10096v1-abstract-short" style="display: inline;"> Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights. However, FL faces a significant challenge in terms of large communication overhead when transmitting these model weights. To address… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10096v1-abstract-full').style.display = 'inline'; document.getElementById('2405.10096v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.10096v1-abstract-full" style="display: none;"> Federated Learning (FL) is an emerging paradigm that holds great promise for privacy-preserving machine learning using distributed data. To enhance privacy, FL can be combined with Differential Privacy (DP), which involves adding Gaussian noise to the model weights. However, FL faces a significant challenge in terms of large communication overhead when transmitting these model weights. To address this issue, quantization is commonly employed. Nevertheless, the presence of quantized Gaussian noise introduces complexities in understanding privacy protection. This research paper investigates the impact of quantization on privacy in FL systems. We examine the privacy guarantees of quantized Gaussian mechanisms using R茅nyi Differential Privacy (RDP). By deriving the privacy budget of quantized Gaussian mechanisms, we demonstrate that lower quantization bit levels provide improved privacy protection. To validate our theoretical findings, we employ Membership Inference Attacks (MIA), which gauge the accuracy of privacy leakage. The numerical results align with our theoretical analysis, confirming that quantization can indeed enhance privacy protection. This study not only enhances our understanding of the correlation between privacy and communication in FL but also underscores the advantages of quantization in preserving privacy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.10096v1-abstract-full').style.display = 'none'; document.getElementById('2405.10096v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages, 5 figures, submitted to 2024 IEEE MeditCom</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.09514">arXiv:2405.09514</a> <span> [<a href="https://arxiv.org/pdf/2405.09514">pdf</a>, <a href="https://arxiv.org/format/2405.09514">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Tackling Distribution Shifts in Task-Oriented Communication with Information Bottleneck </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongru Li</a>, <a href="/search/cs?searchtype=author&query=Shao%2C+J">Jiawei Shao</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.09514v1-abstract-short" style="display: inline;"> Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between training and test data, including domain shift and semantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09514v1-abstract-full').style.display = 'inline'; document.getElementById('2405.09514v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.09514v1-abstract-full" style="display: none;"> Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between training and test data, including domain shift and semantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that the encoded features can generalize to domain-shifted data and detect semanticshifted data, while remaining compact for transmission. In this paper, we propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effective domain-shift generalization and accurate semantic-shift detection without any knowledge of the test data during training. Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature. Furthermore, we enhance the task-oriented communication with the label-dependent feature encoding approach for semanticshift detection which achieves joint gains in IB optimization and detection performance. To avoid the intractable computation of the IB-based objective, we leverage variational approximation to derive a tractable upper bound for optimization. Extensive simulation results on image classification tasks demonstrate that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.09514v1-abstract-full').style.display = 'none'; document.getElementById('2405.09514v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 8 figures, submitted to IEEE for potential publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.08096">arXiv:2405.08096</a> <span> [<a href="https://arxiv.org/pdf/2405.08096">pdf</a>, <a href="https://arxiv.org/ps/2405.08096">ps</a>, <a href="https://arxiv.org/format/2405.08096">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Semantic MIMO Systems for Speech-to-Text Transmission </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Weng%2C+Z">Zhenzi Weng</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/cs?searchtype=author&query=Xie%2C+H">Huiqiang Xie</a>, <a href="/search/cs?searchtype=author&query=Tao%2C+X">Xiaoming Tao</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.08096v2-abstract-short" style="display: inline;"> Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08096v2-abstract-full').style.display = 'inline'; document.getElementById('2405.08096v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.08096v2-abstract-full" style="display: none;"> Semantic communications have been utilized to execute numerous intelligent tasks by transmitting task-related semantic information instead of bits. In this article, we propose a semantic-aware speech-to-text transmission system for the single-user multiple-input multiple-output (MIMO) and multi-user MIMO communication scenarios, named SAC-ST. Particularly, a semantic communication system to serve the speech-to-text task at the receiver is first designed, which compresses the semantic information and generates the low-dimensional semantic features by leveraging the transformer module. In addition, a novel semantic-aware network is proposed to facilitate transmission with high semantic fidelity by identifying the critical semantic information and guaranteeing its accurate recovery. Furthermore, we extend the SAC-ST with a neural network-enabled channel estimation network to mitigate the dependence on accurate channel state information and validate the feasibility of SAC-ST in practical communication environments. Simulation results will show that the proposed SAC-ST outperforms the communication framework without the semantic-aware network for speech-to-text transmission over the MIMO channels in terms of the speech-to-text metrics, especially in the low signal-to-noise regime. Moreover, the SAC-ST with the developed channel estimation network is comparable to the SAC-ST with perfect channel state information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08096v2-abstract-full').style.display = 'none'; document.getElementById('2405.08096v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.04198">arXiv:2405.04198</a> <span> [<a href="https://arxiv.org/pdf/2405.04198">pdf</a>, <a href="https://arxiv.org/format/2405.04198">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+C">Changyuan Zhao</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+I">Dong In Kim</a>, <a href="/search/cs?searchtype=author&query=Xuemin"> Xuemin</a>, <a href="/search/cs?searchtype=author&query=Shen"> Shen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.04198v1-abstract-short" style="display: inline;"> AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational comple… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04198v1-abstract-full').style.display = 'inline'; document.getElementById('2405.04198v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.04198v1-abstract-full" style="display: none;"> AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model's applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework's effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04198v1-abstract-full').style.display = 'none'; document.getElementById('2405.04198v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 4 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.08878">arXiv:2404.08878</a> <span> [<a href="https://arxiv.org/pdf/2404.08878">pdf</a>, <a href="https://arxiv.org/format/2404.08878">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Generative AI Agent for Next-Generation MIMO Design: Fundamentals, Challenges, and Vision </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhe Wang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jiayi Zhang</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+R">Ruichen Zhang</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Ai%2C+B">Bo Ai</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.08878v1-abstract-short" style="display: inline;"> Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. In this paper, we study generative artificial intelligence (AI) agent-enabled next-generation MIMO design. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08878v1-abstract-full').style.display = 'inline'; document.getElementById('2404.08878v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.08878v1-abstract-full" style="display: none;"> Next-generation multiple input multiple output (MIMO) is expected to be intelligent and scalable. In this paper, we study generative artificial intelligence (AI) agent-enabled next-generation MIMO design. Firstly, we provide an overview of the development, fundamentals, and challenges of the next-generation MIMO. Then, we propose the concept of the generative AI agent, which is capable of generating tailored and specialized contents with the aid of large language model (LLM) and retrieval augmented generation (RAG). Next, we comprehensively discuss the features and advantages of the generative AI agent framework. More importantly, to tackle existing challenges of next-generation MIMO, we discuss generative AI agent-enabled next-generation MIMO design, from the perspective of performance analysis, signal processing, and resource allocation. Furthermore, we present two compelling case studies that demonstrate the effectiveness of leveraging the generative AI agent for performance analysis in complex configuration scenarios. These examples highlight how the integration of generative AI agents can significantly enhance the analysis and design of next-generation MIMO systems. Finally, we discuss important potential research future directions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.08878v1-abstract-full').style.display = 'none'; document.getElementById('2404.08878v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 3 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.01875">arXiv:2404.01875</a> <span> [<a href="https://arxiv.org/pdf/2404.01875">pdf</a>, <a href="https://arxiv.org/format/2404.01875">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Satellite Federated Edge Learning: Architecture Design and Convergence Analysis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shi%2C+Y">Yuanming Shi</a>, <a href="/search/cs?searchtype=author&query=Zeng%2C+L">Li Zeng</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+J">Jingyang Zhu</a>, <a href="/search/cs?searchtype=author&query=Zhou%2C+Y">Yong Zhou</a>, <a href="/search/cs?searchtype=author&query=Jiang%2C+C">Chunxiao Jiang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.01875v1-abstract-short" style="display: inline;"> The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model paramet… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01875v1-abstract-full').style.display = 'inline'; document.getElementById('2404.01875v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.01875v1-abstract-full" style="display: none;"> The proliferation of low-earth-orbit (LEO) satellite networks leads to the generation of vast volumes of remote sensing data which is traditionally transferred to the ground server for centralized processing, raising privacy and bandwidth concerns. Federated edge learning (FEEL), as a distributed machine learning approach, has the potential to address these challenges by sharing only model parameters instead of raw data. Although promising, the dynamics of LEO networks, characterized by the high mobility of satellites and short ground-to-satellite link (GSL) duration, pose unique challenges for FEEL. Notably, frequent model transmission between the satellites and ground incurs prolonged waiting time and large transmission latency. This paper introduces a novel FEEL algorithm, named FEDMEGA, tailored to LEO mega-constellation networks. By integrating inter-satellite links (ISL) for intra-orbit model aggregation, the proposed algorithm significantly reduces the usage of low data rate and intermittent GSL. Our proposed method includes a ring all-reduce based intra-orbit aggregation mechanism, coupled with a network flow-based transmission scheme for global model aggregation, which enhances transmission efficiency. Theoretical convergence analysis is provided to characterize the algorithm performance. Extensive simulations show that our FEDMEGA algorithm outperforms existing satellite FEEL algorithms, exhibiting an approximate 30% improvement in convergence rate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.01875v1-abstract-full').style.display = 'none'; document.getElementById('2404.01875v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 15 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00309">arXiv:2404.00309</a> <span> [<a href="https://arxiv.org/pdf/2404.00309">pdf</a>, <a href="https://arxiv.org/format/2404.00309">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Model-Driven Deep Learning for Distributed Detection with Binary Quantization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Guo%2C+W">Wei Guo</a>, <a href="/search/cs?searchtype=author&query=He%2C+M">Meng He</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+C">Chuan Huang</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00309v1-abstract-short" style="display: inline;"> Within the realm of rapidly advancing wireless sensor networks (WSNs), distributed detection assumes a significant role in various practical applications. However, critical challenge lies in maintaining robust detection performance while operating within the constraints of limited bandwidth and energy resources. This paper introduces a novel approach that combines model-driven deep learning (DL) w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00309v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00309v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00309v1-abstract-full" style="display: none;"> Within the realm of rapidly advancing wireless sensor networks (WSNs), distributed detection assumes a significant role in various practical applications. However, critical challenge lies in maintaining robust detection performance while operating within the constraints of limited bandwidth and energy resources. This paper introduces a novel approach that combines model-driven deep learning (DL) with binary quantization to strike a balance between communication overhead and detection performance in WSNs. We begin by establishing the lower bound of detection error probability for distributed detection using the maximum a posteriori (MAP) criterion. Furthermore, we prove the global optimality of employing identical local quantizers across sensors, thereby maximizing the corresponding Chernoff information. Subsequently, the paper derives the minimum MAP detection error probability (MAPDEP) by inplementing identical binary probabilistic quantizers across the sensors. Moreover, the paper establishes the equivalence between utilizing all quantized data and their average as input to the detector at the fusion center (FC). In particular, we derive the Kullback-Leibler (KL) divergence, which measures the difference between the true posterior probability and output of the proposed detector. Leveraging the MAPDEP and KL divergence as loss functions, the paper proposes model-driven DL method to separately train the probability controller module in the quantizer and the detector module at the FC. Numerical results validate the convergence and effectiveness of the proposed method, which achieves near-optimal performance with reduced complexity for Gaussian hypothesis testing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00309v1-abstract-full').style.display = 'none'; document.getElementById('2404.00309v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.13553">arXiv:2402.13553</a> <span> [<a href="https://arxiv.org/pdf/2402.13553">pdf</a>, <a href="https://arxiv.org/format/2402.13553">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> Generative AI for Secure Physical Layer Communications: A Survey </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+C">Changyuan Zhao</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+I">Dong In Kim</a>, <a href="/search/cs?searchtype=author&query=Xuemin"> Xuemin</a>, <a href="/search/cs?searchtype=author&query=Shen"> Shen</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.13553v1-abstract-short" style="display: inline;"> Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, trad… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13553v1-abstract-full').style.display = 'inline'; document.getElementById('2402.13553v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.13553v1-abstract-full" style="display: none;"> Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. Beyond content creation, GAI has significant analytical abilities to learn complex data distribution, offering numerous opportunities to resolve security issues. In the realm of security from physical layer perspectives, traditional AI approaches frequently struggle, primarily due to their limited capacity to dynamically adjust to the evolving physical attributes of transmission channels and the complexity of contemporary cyber threats. This adaptability and analytical depth are precisely where GAI excels. Therefore, in this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We first emphasize the importance of advanced GAI models in this area, including Generative Adversarial Networks (GANs), Autoencoders (AEs), Variational Autoencoders (VAEs), and Diffusion Models (DMs). We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity. Furthermore, we also present future research directions focusing model improvements, multi-scenario deployment, resource-efficient optimization, and secure semantic communication, highlighting the multifaceted potential of GAI to address emerging challenges in secure physical layer communications and sensing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13553v1-abstract-full').style.display = 'none'; document.getElementById('2402.13553v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22pages, 8figs</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10473">arXiv:2402.10473</a> <span> [<a href="https://arxiv.org/pdf/2402.10473">pdf</a>, <a href="https://arxiv.org/format/2402.10473">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+S">Songjie Xie</a>, <a href="/search/cs?searchtype=author&query=Wu%2C+Y">Youlong Wu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jiaxuan Li</a>, <a href="/search/cs?searchtype=author&query=Ding%2C+M">Ming Ding</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.10473v1-abstract-short" style="display: inline;"> As machine learning (ML) becomes more prevalent in human-centric applications, there is a growing emphasis on algorithmic fairness and privacy protection. While previous research has explored these areas as separate objectives, there is a growing recognition of the complex relationship between privacy and fairness. However, previous works have primarily focused on examining the interplay between p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10473v1-abstract-full').style.display = 'inline'; document.getElementById('2402.10473v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10473v1-abstract-full" style="display: none;"> As machine learning (ML) becomes more prevalent in human-centric applications, there is a growing emphasis on algorithmic fairness and privacy protection. While previous research has explored these areas as separate objectives, there is a growing recognition of the complex relationship between privacy and fairness. However, previous works have primarily focused on examining the interplay between privacy and fairness through empirical investigations, with limited attention given to theoretical exploration. This study aims to bridge this gap by introducing a theoretical framework that enables a comprehensive examination of their interrelation. We shall develop and analyze an information bottleneck (IB) based information obfuscation method with local differential privacy (LDP) for fair representation learning. In contrast to many empirical studies on fairness in ML, we show that the incorporation of LDP randomizers during the encoding process can enhance the fairness of the learned representation. Our analysis will demonstrate that the disclosure of sensitive information is constrained by the privacy budget of the LDP randomizer, thereby enabling the optimization process within the IB framework to effectively suppress sensitive information while preserving the desired utility through obfuscation. Based on the proposed method, we further develop a variational representation encoding approach that simultaneously achieves fairness and LDP. Our variational encoding approach offers practical advantages. It is trained using a non-adversarial method and does not require the introduction of any variational prior. Extensive experiments will be presented to validate our theoretical results and demonstrate the ability of our proposed approach to achieve both LDP and fairness while preserving adequate utility. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10473v1-abstract-full').style.display = 'none'; document.getElementById('2402.10473v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.06942">arXiv:2402.06942</a> <span> [<a href="https://arxiv.org/pdf/2402.06942">pdf</a>, <a href="https://arxiv.org/format/2402.06942">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+J">Jiacheng Wang</a>, <a href="/search/cs?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+I">Dong In Kim</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.06942v1-abstract-short" style="display: inline;"> The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06942v1-abstract-full').style.display = 'inline'; document.getElementById('2402.06942v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.06942v1-abstract-full" style="display: none;"> The advancement of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The widespread of these applications relies on the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE, GAI faces challenges in resource consumption when deployed on user devices. This paper proposes mobile edge networks supported MoE-based GAI. We first review the MoE from traditional AI and GAI perspectives, including structure, principles, and applications. We then propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. We discuss challenges in this process and introduce a deep reinforcement learning based algorithm to select edge devices for subtask execution. Experimental results will show that our framework not only facilitates GAI's deployment on resource-limited devices but also generates higher-quality content compared to methods without edge network support. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06942v1-abstract-full').style.display = 'none'; document.getElementById('2402.06942v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.11155">arXiv:2401.11155</a> <span> [<a href="https://arxiv.org/pdf/2401.11155">pdf</a>, <a href="https://arxiv.org/format/2401.11155">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Deep Learning-Based Adaptive Joint Source-Channel Coding using Hypernetworks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xie%2C+S">Songjie Xie</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Li%2C+H">Hongru Li</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y+A">Ying-Jun Angela Zhang</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.11155v1-abstract-short" style="display: inline;"> Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for {the} next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained under specific channel conditions. In this paper, we propose a generic framework for channel-adaptive DJSCC by utilizing hypernetworks. To tailor the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11155v1-abstract-full').style.display = 'inline'; document.getElementById('2401.11155v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.11155v1-abstract-full" style="display: none;"> Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for {the} next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained under specific channel conditions. In this paper, we propose a generic framework for channel-adaptive DJSCC by utilizing hypernetworks. To tailor the hypernetwork-based framework for communication systems, we propose a memory-efficient hypernetwork parameterization and then develop a channel-adaptive DJSCC network, named Hyper-AJSCC. Compared with existing adaptive DJSCC based on the attention mechanism, Hyper-AJSCC introduces much fewer parameters and can be seamlessly combined with various existing DJSCC networks without any substantial modifications to their neural network architecture. Extensive experiments demonstrate the better adaptability to channel conditions and higher memory efficiency of Hyper-AJSCC compared with state-of-the-art baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.11155v1-abstract-full').style.display = 'none'; document.getElementById('2401.11155v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.09886">arXiv:2401.09886</a> <span> [<a href="https://arxiv.org/pdf/2401.09886">pdf</a>, <a href="https://arxiv.org/format/2401.09886">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wu%2C+Q">Qiong Wu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+W">Wenhua Wang</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+Q">Qiang Fan</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+H">Huiling Zhu</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.09886v2-abstract-short" style="display: inline;"> Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09886v2-abstract-full').style.display = 'inline'; document.getElementById('2401.09886v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.09886v2-abstract-full" style="display: none;"> Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.09886v2-abstract-full').style.display = 'none'; document.getElementById('2401.09886v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been submitted to IEEE TNSM. The source code has been released at: https://github.com/qiongwu86/Edge-Caching-Based-on-Multi-Agent-Deep-Reinforcement-Learning-and-Federated-Learning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.07764">arXiv:2401.07764</a> <span> [<a href="https://arxiv.org/pdf/2401.07764">pdf</a>, <a href="https://arxiv.org/format/2401.07764">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+M">Minrui Xu</a>, <a href="/search/cs?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/cs?searchtype=author&query=Mao%2C+S">Shiwen Mao</a>, <a href="/search/cs?searchtype=author&query=Han%2C+Z">Zhu Han</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+D+I">Dong In Kim</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.07764v2-abstract-short" style="display: inline;"> AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment. Deploying LLM agents in 6G networks enables users to access previously expensive AI assistant services via mobile devices democratically, thereby reduci… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.07764v2-abstract-full').style.display = 'inline'; document.getElementById('2401.07764v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.07764v2-abstract-full" style="display: none;"> AI agents based on multimodal large language models (LLMs) are expected to revolutionize human-computer interaction and offer more personalized assistant services across various domains like healthcare, education, manufacturing, and entertainment. Deploying LLM agents in 6G networks enables users to access previously expensive AI assistant services via mobile devices democratically, thereby reducing interaction latency and better preserving user privacy. Nevertheless, the limited capacity of mobile devices constrains the effectiveness of deploying and executing local LLMs, which necessitates offloading complex tasks to global LLMs running on edge servers during long-horizon interactions. In this article, we propose a split learning system for LLM agents in 6G networks leveraging the collaboration between mobile devices and edge servers, where multiple LLMs with different roles are distributed across mobile devices and edge servers to perform user-agent interactive tasks collaboratively. In the proposed system, LLM agents are split into perception, grounding, and alignment modules, facilitating inter-module communications to meet extended user requirements on 6G network functions, including integrated sensing and communication, digital twins, and task-oriented communications. Furthermore, we introduce a novel model caching algorithm for LLMs within the proposed system to improve model utilization in context, thus reducing network costs of the collaborative mobile and edge LLM agents. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.07764v2-abstract-full').style.display = 'none'; document.getElementById('2401.07764v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.16909">arXiv:2312.16909</a> <span> [<a href="https://arxiv.org/pdf/2312.16909">pdf</a>, <a href="https://arxiv.org/format/2312.16909">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> A GAN-based Semantic Communication for Text without CSI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mao%2C+J">Jin Mao</a>, <a href="/search/cs?searchtype=author&query=Xiong%2C+K">Ke Xiong</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+M">Ming Liu</a>, <a href="/search/cs?searchtype=author&query=Qin%2C+Z">Zhijin Qin</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+W">Wei Chen</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+P">Pingyi Fan</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled Ben Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.16909v1-abstract-short" style="display: inline;"> Recently, semantic communication (SC) has been regarded as one of the potential paradigms of 6G. Current SC frameworks require channel state information (CSI) to handle severe signal distortion induced by channel fading. Since the channel estimation overhead for obtaining CSI cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn't r… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.16909v1-abstract-full').style.display = 'inline'; document.getElementById('2312.16909v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.16909v1-abstract-full" style="display: none;"> Recently, semantic communication (SC) has been regarded as one of the potential paradigms of 6G. Current SC frameworks require channel state information (CSI) to handle severe signal distortion induced by channel fading. Since the channel estimation overhead for obtaining CSI cannot be neglected, we therefore propose a generative adversarial network (GAN) based SC framework (Ti-GSC) that doesn't require CSI. In Ti-GSC, two main modules, i.e., an autoencoder-based encoder-decoder module (AEDM) and a GAN-based signal distortion suppression module (GSDSM) are included where AEDM first encodes the data at the source before transmission, and then GSDSM suppresses the distortion of the received signals in both syntactic and semantic dimensions at the destination. At last, AEDM decodes the distortion-suppressed signal at the destination. To measure signal distortion, syntactic distortion and semantic distortion terms are newly added to the total loss function. To achieve better training results, joint optimization-based training (JOT) and alternating optimization-based training (AOT) are designed for the proposed Ti-GSC. Experimental results show that JOT is more efficient for Ti-GSC. Moreover, without CSI, bilingual evaluation understudy (BLEU) score achieved by Ti-GSC is about 40% and 62% higher than that achieved by existing SC frameworks in Rician and Rayleigh fading, respectively. (*Due to the notification of arXiv "The Abstract field cannot be longer than 1,920 characters", the appeared Abstract is shortened. For the full Abstract, please download the Article.) <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.16909v1-abstract-full').style.display = 'none'; document.getElementById('2312.16909v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.13683">arXiv:2312.13683</a> <span> [<a href="https://arxiv.org/pdf/2312.13683">pdf</a>, <a href="https://arxiv.org/format/2312.13683">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> </div> <p class="title is-5 mathjax"> Joint Channel Estimation and Cooperative Localization for Near-Field Ultra-Massive MIMO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cao%2C+R">Ruoxiao Cao</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Huang%2C+K">Kaibin Huang</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Gong%2C+Y">Yi Gong</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.13683v1-abstract-short" style="display: inline;"> The next-generation (6G) wireless networks are expected to provide not only seamless and high data-rate communications, but also ubiquitous sensing services. By providing vast spatial degrees of freedom (DoFs), ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for both sensing and communications in 6G. However, the adoption of UM-MIMO leads to a shift from the far… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13683v1-abstract-full').style.display = 'inline'; document.getElementById('2312.13683v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.13683v1-abstract-full" style="display: none;"> The next-generation (6G) wireless networks are expected to provide not only seamless and high data-rate communications, but also ubiquitous sensing services. By providing vast spatial degrees of freedom (DoFs), ultra-massive multiple-input multiple-output (UM-MIMO) technology is a key enabler for both sensing and communications in 6G. However, the adoption of UM-MIMO leads to a shift from the far field to the near field in terms of the electromagnetic propagation, which poses novel challenges in system design. Specifically, near-field effects introduce highly non-linear spherical wave models that render existing designs based on plane wave assumptions ineffective. In this paper, we focus on two crucial tasks in sensing and communications, respectively, i.e., localization and channel estimation, and investigate their joint design by exploring the near-field propagation characteristics, achieving mutual benefits between two tasks. In addition, multiple base stations (BSs) are leveraged to collaboratively facilitate a cooperative localization framework. To address the joint channel estimation and cooperative localization problem for near-field UM-MIMO systems, we propose a variational Newtonized near-field channel estimation (VNNCE) algorithm and a Gaussian fusion cooperative localization (GFCL) algorithm. The VNNCE algorithm exploits the spatial DoFs provided by the near-field channel to obtain position-related soft information, while the GFCL algorithm fuses this soft information to achieve more accurate localization. Additionally, we introduce a joint architecture that seamlessly integrates channel estimation and cooperative localization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13683v1-abstract-full').style.display = 'none'; document.getElementById('2312.13683v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submit to JSAC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.10438">arXiv:2312.10438</a> <span> [<a href="https://arxiv.org/pdf/2312.10438">pdf</a>, <a href="https://arxiv.org/format/2312.10438">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/JSTSP.2024.3414137">10.1109/JSTSP.2024.3414137 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Bayes-Optimal Unsupervised Learning for Channel Estimation in Near-Field Holographic MIMO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yu%2C+W">Wentao Yu</a>, <a href="/search/cs?searchtype=author&query=He%2C+H">Hengtao He</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/cs?searchtype=author&query=Song%2C+S">Shenghui Song</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+J">Jun Zhang</a>, <a href="/search/cs?searchtype=author&query=Murch%2C+R">Ross Murch</a>, <a href="/search/cs?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.10438v2-abstract-short" style="display: inline;"> Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas within a compact space to fully exploit the potentials of the electromagnetic (EM) channel. Nevertheless, the benefits of HMIMO systems cannot be fully unleashed without an efficient means to estimate the high-dimensional cha… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10438v2-abstract-full').style.display = 'inline'; document.getElementById('2312.10438v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.10438v2-abstract-full" style="display: none;"> Holographic MIMO (HMIMO) is being increasingly recognized as a key enabling technology for 6G wireless systems through the deployment of an extremely large number of antennas within a compact space to fully exploit the potentials of the electromagnetic (EM) channel. Nevertheless, the benefits of HMIMO systems cannot be fully unleashed without an efficient means to estimate the high-dimensional channel, whose distribution becomes increasingly complicated due to the accessibility of the near-field region. In this paper, we address the fundamental challenge of designing a low-complexity Bayes-optimal channel estimator in near-field HMIMO systems operating in unknown EM environments. The core idea is to estimate the HMIMO channels solely based on the Stein's score function of the received pilot signals and an estimated noise level, without relying on priors or supervision that is not feasible in practical deployment. A neural network is trained with the unsupervised denoising score matching objective to learn the parameterized score function. Meanwhile, a principal component analysis (PCA)-based algorithm is proposed to estimate the noise level leveraging the low-rank near-field spatial correlation. Building upon these techniques, we develop a Bayes-optimal score-based channel estimator for fully-digital HMIMO transceivers in a closed form. The optimal score-based estimator is also extended to hybrid analog-digital HMIMO systems by incorporating it into a low-complexity message passing algorithm. The (quasi-) Bayes-optimality of the proposed estimators is validated both in theory and by extensive simulation results. In addition to optimality, it is shown that our proposal is robust to various mismatches and can quickly adapt to dynamic EM environments in an online manner thanks to its unsupervised nature, demonstrating its potential in real-world deployment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10438v2-abstract-full').style.display = 'none'; document.getElementById('2312.10438v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 7 figures, 3 tables, accepted by IEEE Journal of Selected Topics in Signal Processing</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Letaief%2C+K+B&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> <li> <a 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