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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=Cui%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Cui%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Cui%2C+S&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </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/2503.18078">arXiv:2503.18078</a> <span> [<a href="https://arxiv.org/pdf/2503.18078">pdf</a>, <a href="https://arxiv.org/format/2503.18078">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> </div> </div> <p class="title is-5 mathjax"> GenMetaLoc: Learning to Learn Environment-Aware Fingerprint Generation for Sample Efficient Wireless Localization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jun Gao</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+F">Feng Yin</a>, <a href="/search/eess?searchtype=author&query=Yan%2C+W">Wenzhong Yan</a>, <a href="/search/eess?searchtype=author&query=Kong%2C+Q">Qinglei Kong</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2503.18078v1-abstract-short" style="display: inline;"> Existing fingerprinting-based localization methods often require extensive data collection and struggle to generalize to new environments. In contrast to previous environment-unknown MetaLoc, we propose GenMetaLoc in this paper, which first introduces meta-learning to enable the generation of dense fingerprint databases from an environment-aware perspective. In the model aspect, the learning-to-le… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18078v1-abstract-full').style.display = 'inline'; document.getElementById('2503.18078v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.18078v1-abstract-full" style="display: none;"> Existing fingerprinting-based localization methods often require extensive data collection and struggle to generalize to new environments. In contrast to previous environment-unknown MetaLoc, we propose GenMetaLoc in this paper, which first introduces meta-learning to enable the generation of dense fingerprint databases from an environment-aware perspective. In the model aspect, the learning-to-learn mechanism accelerates the fingerprint generation process by facilitating rapid adaptation to new environments with minimal data. Additionally, we incorporate 3D point cloud data from the first Fresnel zone between the transmitter and receiver, which describes the obstacles distribution in the environment and serves as a condition to guide the diffusion model in generating more accurate fingerprints. In the data processing aspect, unlike most studies that focus solely on channel state information (CSI) amplitude or phase, we present a comprehensive processing that addresses both, correcting errors from WiFi hardware limitations such as amplitude discrepancies and frequency offsets. For the data collection platform, we develop an uplink wireless localization system that leverages the sensing capabilities of existing commercial WiFi devices and mobile phones, thus reducing the need for additional deployment costs. Experimental results on real datasets show that our framework outperforms baseline methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18078v1-abstract-full').style.display = 'none'; document.getElementById('2503.18078v1-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> 23 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/2503.00298">arXiv:2503.00298</a> <span> [<a href="https://arxiv.org/pdf/2503.00298">pdf</a>, <a href="https://arxiv.org/format/2503.00298">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"> Energy-Efficient Edge Inference in Integrated Sensing, Communication, and Computation Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yao%2C+J">Jiacheng Yao</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+W">Wei Xu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+K">Kaibin Huang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2503.00298v1-abstract-short" style="display: inline;"> Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems (ICPS). However, the constrained energy supply at edge devices has emerged as a critical bottleneck. In this paper, we propose a novel energy-efficient ISCC fra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.00298v1-abstract-full').style.display = 'inline'; document.getElementById('2503.00298v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.00298v1-abstract-full" style="display: none;"> Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems (ICPS). However, the constrained energy supply at edge devices has emerged as a critical bottleneck. In this paper, we propose a novel energy-efficient ISCC framework for AI inference at resource-constrained edge devices, where adjustable split inference, model pruning, and feature quantization are jointly designed to adapt to diverse task requirements. A joint resource allocation design problem for the proposed ISCC framework is formulated to minimize the energy consumption under stringent inference accuracy and latency constraints. To address the challenge of characterizing inference accuracy, we derive an explicit approximation for it by analyzing the impact of sensing, communication, and computation processes on the inference performance. Building upon the analytical results, we propose an iterative algorithm employing alternating optimization to solve the resource allocation problem. In each subproblem, the optimal solutions are available by respectively applying a golden section search method and checking the Karush-Kuhn-Tucker (KKT) conditions, thereby ensuring the convergence to a local optimum of the original problem. Numerical results demonstrate the effectiveness of the proposed ISCC design, showing a significant reduction in energy consumption of up to 40% compared to existing methods, particularly in low-latency scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.00298v1-abstract-full').style.display = 'none'; document.getElementById('2503.00298v1-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 February, 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">Accepted by IEEE 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/2502.08276">arXiv:2502.08276</a> <span> [<a href="https://arxiv.org/pdf/2502.08276">pdf</a>, <a href="https://arxiv.org/ps/2502.08276">ps</a>, <a href="https://arxiv.org/format/2502.08276">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> </div> </div> <p class="title is-5 mathjax"> Higher-order Laplacian dynamics on hypergraphs with cooperative and antagonistic interactions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Chencheng Zhang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+B">Bin Jiang</a>, <a href="/search/eess?searchtype=author&query=Kojakhmetov%2C+H+J">Hildeberto Jard贸n Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2502.08276v1-abstract-short" style="display: inline;"> Laplacian dynamics on a signless graph characterize a class of linear interactions, where pairwise cooperative interactions between all agents lead to the convergence to a common state. On a structurally balanced signed graph, the agents converge to values of the same magnitude but opposite signs (bipartite consensus), as illustrated by the well-known Altafini model. These interactions have been m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08276v1-abstract-full').style.display = 'inline'; document.getElementById('2502.08276v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.08276v1-abstract-full" style="display: none;"> Laplacian dynamics on a signless graph characterize a class of linear interactions, where pairwise cooperative interactions between all agents lead to the convergence to a common state. On a structurally balanced signed graph, the agents converge to values of the same magnitude but opposite signs (bipartite consensus), as illustrated by the well-known Altafini model. These interactions have been modeled using traditional graphs, where the relationships between agents are always pairwise. In comparison, higher-order networks (such as hypergraphs), offer the possibility to capture more complex, group-wise interactions among agents. This raises a natural question: can collective behavior be analyzed by using hypergraphs? The answer is affirmative. In this paper, higher-order Laplacian dynamics on signless hypergraphs are first introduced and various collective convergence behaviors are investigated, in the framework of homogeneous and non-homogeneous polynomial systems. Furthermore, by employing gauge transformations and leveraging tensor similarities, we extend these dynamics to signed hypergraphs, drawing parallels to the Altafini model. Moreover, we explore non-polynomial interaction functions within this framework. The theoretical results are demonstrated through several numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.08276v1-abstract-full').style.display = 'none'; document.getElementById('2502.08276v1-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 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/2501.17876">arXiv:2501.17876</a> <span> [<a href="https://arxiv.org/pdf/2501.17876">pdf</a>, <a href="https://arxiv.org/format/2501.17876">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"> SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mo%2C+H">Hao Mo</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yaping Sun</a>, <a href="/search/eess?searchtype=author&query=Yao%2C+S">Shumin Yao</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Hao Chen</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhiyong Chen</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xiaodong Xu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+N">Nan Ma</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+M">Meixia Tao</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2501.17876v2-abstract-short" style="display: inline;"> Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17876v2-abstract-full').style.display = 'inline'; document.getElementById('2501.17876v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.17876v2-abstract-full" style="display: none;"> Score-based diffusion models represent a significant variant within the diffusion model family and have seen extensive application in the increasingly popular domain of generative tasks. Recent investigations have explored the denoising potential of diffusion models in semantic communications. However, in previous paradigms, noise distortion in the diffusion process does not match precisely with digital channel noise characteristics. In this work, we introduce the Score-Based Channel Denoising Model (SCDM) for Digital Semantic Communications (DSC). SCDM views the distortion of constellation symbol sequences in digital transmission as a score-based forward diffusion process. We design a tailored forward noise corruption to align digital channel noise properties in the training phase. During the inference stage, the well-trained SCDM can effectively denoise received semantic symbols under various SNR conditions, reducing the difficulty for the semantic decoder in extracting semantic information from the received noisy symbols and thereby enhancing the robustness of the reconstructed semantic information. Experimental results show that SCDM outperforms the baseline model in PSNR, SSIM, and MSE metrics, particularly at low SNR levels. Moreover, SCDM reduces storage requirements by a factor of 7.8. This efficiency in storage, combined with its robust denoising capability, makes SCDM a practical solution for DSC across diverse channel conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.17876v2-abstract-full').style.display = 'none'; document.getElementById('2501.17876v2-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.09052">arXiv:2501.09052</a> <span> [<a href="https://arxiv.org/pdf/2501.09052">pdf</a>, <a href="https://arxiv.org/format/2501.09052">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</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/10.1007/s11263-025-02363-0">10.1007/s11263-025-02363-0 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuang Cui</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yi Li</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jiangmeng Li</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+X">Xiongxin Tang</a>, <a href="/search/eess?searchtype=author&query=Su%2C+B">Bing Su</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+F">Fanjiang Xu</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+H">Hui Xiong</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="2501.09052v2-abstract-short" style="display: inline;"> Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09052v2-abstract-full').style.display = 'inline'; document.getElementById('2501.09052v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.09052v2-abstract-full" style="display: none;"> Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, which is identified as the heterogeneity of lens-specific point spread functions. Empirical evidence supports this finding, motivating us to employ a continual test-time adaptation (CTTA) paradigm for SIDD. However, traditional CTTA methods, which primarily rely on entropy minimization, cannot sufficiently explore task-dependent information for pixel-level regression tasks like SIDD. To address this issue, we propose a novel Siamese networks-based continual test-time adaptation framework, which adapts source models to continuously changing target domains only requiring unlabeled target data in an online manner. To further mitigate semantically erroneous textures introduced by source SIDD models under severe degradation, we revisit the learning paradigm through a structural causal model and propose Causal Siamese networks (CauSiam). Our method leverages large-scale pre-trained vision-language models to derive discriminative universal semantic priors and integrates these priors into Siamese networks, ensuring causal identifiability between blurry inputs and restored images. Extensive experiments demonstrate that CauSiam effectively improves the generalization performance of existing SIDD methods in continuously changing domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.09052v2-abstract-full').style.display = 'none'; document.getElementById('2501.09052v2-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> 23 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 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">Journal ref:</span> International Journal of Computer Vision 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.07428">arXiv:2412.07428</a> <span> [<a href="https://arxiv.org/pdf/2412.07428">pdf</a>, <a href="https://arxiv.org/format/2412.07428">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> When UAV Meets Federated Learning: Latency Minimization via Joint Trajectory Design and Resource Allocation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuhui Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenchao Liu</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+H">Huijun Xing</a>, <a href="/search/eess?searchtype=author&query=Gui%2C+G">Gui Gui</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yanyan Shen</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2412.07428v1-abstract-short" style="display: inline;"> Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources. Despite its benefits, the efficiency of FL is often restricted by the communication quality between IoT devices and the central server. To address this issue, we introduce an innovative approach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07428v1-abstract-full').style.display = 'inline'; document.getElementById('2412.07428v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.07428v1-abstract-full" style="display: none;"> Federated learning (FL) has emerged as a pivotal solution for training machine learning models over wireless networks, particularly for Internet of Things (IoT) devices with limited computation resources. Despite its benefits, the efficiency of FL is often restricted by the communication quality between IoT devices and the central server. To address this issue, we introduce an innovative approach by deploying an unmanned aerial vehicle (UAV) as a mobile FL server to enhance the training process of FL. By leveraging the UAV's maneuverability, we establish robust line-of-sight connections with IoT devices, significantly improving communication capacity. To improve the overall training efficiency, we formulate a latency minimization problem by jointly optimizing the bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's trajectory. Then, an efficient alternating optimization algorithm is developed to solve it efficiently. Furthermore, we analyze the convergence and computational complexity of the proposed algorithm. Finally, numerical results demonstrate that our proposed scheme not only outperforms existing benchmark schemes in terms of latency but also achieves training efficiency that closely approximate the ideal scenario. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07428v1-abstract-full').style.display = 'none'; document.getElementById('2412.07428v1-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 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">This manuscript has been submitted to IEEE</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.16380">arXiv:2411.16380</a> <span> [<a href="https://arxiv.org/pdf/2411.16380">pdf</a>, <a href="https://arxiv.org/format/2411.16380">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</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="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jiang%2C+Y">Yuncheng Jiang</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+C">Chun-Mei Feng</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+J">Jun Wei</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zixun Zhang</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Y">Yiwen Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yunbi Liu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+R">Rui Sun</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+X">Xuemei Tang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+J">Juan Du</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+X">Xiang Wan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yong Xu</a>, <a href="/search/eess?searchtype=author&query=Du%2C+B">Bo Du</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+X">Xin Gao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+G">Guangyu Wang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+S">Shaohua Zhou</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Goh%2C+R+S+M">Rick Siow Mong Goh</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yong Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zhen 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="2411.16380v1-abstract-short" style="display: inline;"> Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16380v1-abstract-full').style.display = 'inline'; document.getElementById('2411.16380v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.16380v1-abstract-full" style="display: none;"> Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, conventional ultrasound diagnostics face several limitations, including high dependence on physician expertise and suboptimal image quality, which complicates interpretation and increases the likelihood of diagnostic errors. Artificial intelligence (AI) has emerged as a promising solution to enhance clinical diagnosis, particularly in detecting abnormalities across various biomedical imaging modalities. Nonetheless, current AI models for ultrasound imaging face critical challenges. First, these models often require large volumes of labeled medical data, raising concerns over patient privacy breaches. Second, most existing models are task-specific, which restricts their broader clinical utility. To overcome these challenges, we present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries, leveraging a dataset of over 1 million ultrasound images covering 19 organs and 10 ultrasound modalities. This extensive and diverse data, combined with a secure training framework, enables UltraFedFM to exhibit strong generalization and diagnostic capabilities. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation. Notably, UltraFedFM surpasses the diagnostic accuracy of mid-level ultrasonographers and matches the performance of expert-level sonographers in the joint diagnosis of 8 common systemic diseases. These findings indicate that UltraFedFM can significantly enhance clinical diagnostics while safeguarding patient privacy, marking an advancement in AI-driven ultrasound imaging for future clinical applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.16380v1-abstract-full').style.display = 'none'; document.getElementById('2411.16380v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.06687">arXiv:2411.06687</a> <span> [<a href="https://arxiv.org/pdf/2411.06687">pdf</a>, <a href="https://arxiv.org/format/2411.06687">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> </div> </div> <p class="title is-5 mathjax"> An Overview on IRS-Enabled Sensing and Communications for 6G: Architectures, Fundamental Limits, and Joint Beamforming Designs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Song%2C+X">Xianxin Song</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuan Fang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+F">Feng Wang</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+Z">Zixiang Ren</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Ye Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fan Liu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2411.06687v1-abstract-short" style="display: inline;"> This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's (B… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06687v1-abstract-full').style.display = 'inline'; document.getElementById('2411.06687v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.06687v1-abstract-full" style="display: none;"> This paper presents an overview on intelligent reflecting surface (IRS)-enabled sensing and communication for the forthcoming sixth-generation (6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication (S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's (BS's) non-line-of-sight (NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication (ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.06687v1-abstract-full').style.display = 'none'; document.getElementById('2411.06687v1-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 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">22 pages,7 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/2411.03127">arXiv:2411.03127</a> <span> [<a href="https://arxiv.org/pdf/2411.03127">pdf</a>, <a href="https://arxiv.org/format/2411.03127">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="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"> Receiver-Centric Generative Semantic Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+X">Xunze Liu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yifei Sun</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaorui Wang</a>, <a href="/search/eess?searchtype=author&query=You%2C+L">Lizhao You</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+H">Haoyuan Pan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+F">Fangxin Wang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2411.03127v2-abstract-short" style="display: inline;"> This paper investigates semantic communications between a transmitter and a receiver, where original data, such as videos of interest to the receiver, is stored at the transmitter. Although significant process has been made in semantic communications, a fundamental design problem is that the semantic information is extracted based on certain criteria at the transmitter alone, without considering t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03127v2-abstract-full').style.display = 'inline'; document.getElementById('2411.03127v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.03127v2-abstract-full" style="display: none;"> This paper investigates semantic communications between a transmitter and a receiver, where original data, such as videos of interest to the receiver, is stored at the transmitter. Although significant process has been made in semantic communications, a fundamental design problem is that the semantic information is extracted based on certain criteria at the transmitter alone, without considering the receiver's specific information needs. As a result, critical information of primary concern to the receiver may be lost. In such cases, the semantic transmission becomes meaningless to the receiver, as all received information is irrelevant to its interests. To solve this problem, this paper presents a receiver-centric generative semantic communication system, where each transmission is initialized by the receiver. Specifically, the receiver first sends its request for the desired semantic information to the transmitter at the start of each transmission. Then, the transmitter extracts the required semantic information accordingly. A key challenge is how the transmitter understands the receiver's requests for semantic information and extracts the required semantic information in a reasonable and robust manner. We address this challenge by designing a well-structured framework and leveraging off-the-shelf generative AI products, such as GPT-4, along with several specialized tools for detection and estimation. Evaluation results demonstrate the feasibility and effectiveness of the proposed new semantic communication system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.03127v2-abstract-full').style.display = 'none'; document.getElementById('2411.03127v2-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">v1</span> submitted 5 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">Demo video has been made available at: https://goo.su/dUnAT</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.02349">arXiv:2411.02349</a> <span> [<a href="https://arxiv.org/pdf/2411.02349">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Drone Data Analytics for Measuring Traffic Metrics at Intersections in High-Density Areas </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pu%2C+Q">Qingwen Pu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Y">Yuan Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Junqing Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Hong Yang</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+K">Kun Xie</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shunlai Cui</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="2411.02349v2-abstract-short" style="display: inline;"> This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. This research has enhanced the YOLOUAV model to enable precise target recognition on unmanned aerial vehicle (UAV) datasets. An automated calibration algorithm is presented to create a functio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02349v2-abstract-full').style.display = 'inline'; document.getElementById('2411.02349v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.02349v2-abstract-full" style="display: none;"> This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. This research has enhanced the YOLOUAV model to enable precise target recognition on unmanned aerial vehicle (UAV) datasets. An automated calibration algorithm is presented to create a functional dataset in high-density traffic flows, which saves human and material resources. This algorithm can capture up to 200 vehicles per frame while accurately tracking over 1 million road users, including cars, buses, and trucks. Moreover, the dataset has recorded over 50,000 complete lane changes. It is the largest publicly available road user trajectories in high-density urban intersections. Furthermore, this paper updates speed and acceleration algorithms based on UAV elevation and implements a UAV offset correction algorithm. A case study demonstrates the usefulness of the proposed methods, showing essential parameters to evaluate intersections and traffic conditions in traffic engineering. The model can track more than 200 vehicles of different types simultaneously in highly dense traffic on an urban intersection in Hohhot, generating heatmaps based on spatial-temporal traffic flow data and locating traffic conflicts by conducting lane change analysis and surrogate measures. With the diverse data and high accuracy of results, this study aims to advance research and development of UAVs in transportation significantly. The High-Density Intersection Dataset is available for download at https://github.com/Qpu523/High-density-Intersection-Dataset. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.02349v2-abstract-full').style.display = 'none'; document.getElementById('2411.02349v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 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">30 pages,14 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68-11 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.4.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03459">arXiv:2410.03459</a> <span> [<a href="https://arxiv.org/pdf/2410.03459">pdf</a>, <a href="https://arxiv.org/format/2410.03459">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</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="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Generative Semantic Communication for Text-to-Speech Synthesis </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+J">Jiahao Zheng</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+P">Peng Xu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+Z">Zhihao Yuan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+F">Fangxin Wang</a>, <a href="/search/eess?searchtype=author&query=Gui%2C+G">Gui Gui</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2410.03459v1-abstract-short" style="display: inline;"> Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic communication methods primarily focus on data reconstruction tasks, which may not be efficient for emerging generative tasks such as text-to-speech (TTS) synthesis. To address this limitation, this paper develops a nove… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03459v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03459v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03459v1-abstract-full" style="display: none;"> Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic communication methods primarily focus on data reconstruction tasks, which may not be efficient for emerging generative tasks such as text-to-speech (TTS) synthesis. To address this limitation, this paper develops a novel generative semantic communication framework for TTS synthesis, leveraging generative artificial intelligence technologies. Firstly, we utilize a pre-trained large speech model called WavLM and the residual vector quantization method to construct two semantic knowledge bases (KBs) at the transmitter and receiver, respectively. The KB at the transmitter enables effective semantic extraction, while the KB at the receiver facilitates lifelike speech synthesis. Then, we employ a transformer encoder and a diffusion model to achieve efficient semantic coding without introducing significant communication overhead. Finally, numerical results demonstrate that our framework achieves much higher fidelity for the generated speech than four baselines, in both cases with additive white Gaussian noise channel and Rayleigh fading channel. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03459v1-abstract-full').style.display = 'none'; document.getElementById('2410.03459v1-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 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">The paper has been accepted by IEEE Globecom Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.14782">arXiv:2409.14782</a> <span> [<a href="https://arxiv.org/pdf/2409.14782">pdf</a>, <a href="https://arxiv.org/format/2409.14782">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> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient Multi-UAV-Enabled MEC Systems with Space-Air-Ground Integrated Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenchao Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuhui Zhang</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yanyan Shen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuqiang Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+B">Bo Yang</a>, <a href="/search/eess?searchtype=author&query=Guan%2C+X">Xinping Guan</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2409.14782v1-abstract-short" style="display: inline;"> With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) sup… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14782v1-abstract-full').style.display = 'inline'; document.getElementById('2409.14782v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.14782v1-abstract-full" style="display: none;"> With the development of artificial intelligence integrated next-generation communication networks, mobile users (MUs) are increasingly demanding the efficient processing of computation-intensive and latency-sensitive tasks. However, existing mobile computing networks struggle to support the rapidly growing computational needs of the MUs. Fortunately, space-air-ground integrated network (SAGIN) supported mobile edge computing (MEC) is regarded as an effective solution, offering the MUs multi-tier and efficient computing services. In this paper, we consider an SAGIN supported MEC system, where a low Earth orbit satellite and multiple unmanned aerial vehicles (UAVs) are dispatched to provide computing services for MUs. An energy efficiency maximization problem is formulated, with the joint optimization of the MU-UAV association, the UAV trajectory, the task offloading decision, the computing frequency, and the transmission power control. Since the problem is non-convex, we decompose it into four subproblems, and propose an alternating optimization based algorithm to solve it. Simulation results confirm that the proposed algorithm outperforms the benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.14782v1-abstract-full').style.display = 'none'; document.getElementById('2409.14782v1-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> 23 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 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/2409.13696">arXiv:2409.13696</a> <span> [<a href="https://arxiv.org/pdf/2409.13696">pdf</a>, <a href="https://arxiv.org/format/2409.13696">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Implicit Neural Representation for Sparse-view Photoacoustic Computed Tomography </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yao%2C+B">Bowei Yao</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shilong Cui</a>, <a href="/search/eess?searchtype=author&query=Dai%2C+H">Haizhao Dai</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qing Wu</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Y">Youshen Xiao</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Fei Gao</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+J">Jingyi Yu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yuyao Zhang</a>, <a href="/search/eess?searchtype=author&query=Cai%2C+X">Xiran Cai</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="2409.13696v1-abstract-short" style="display: inline;"> High-quality imaging in photoacoustic computed tomography (PACT) usually requires a high-channel count system for dense spatial sampling around the object to avoid aliasing-related artefacts. To reduce system complexity, various image reconstruction approaches, such as model-based (MB) and deep learning based methods, have been explored to mitigate the artefacts associated with sparse-view acquisi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13696v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13696v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13696v1-abstract-full" style="display: none;"> High-quality imaging in photoacoustic computed tomography (PACT) usually requires a high-channel count system for dense spatial sampling around the object to avoid aliasing-related artefacts. To reduce system complexity, various image reconstruction approaches, such as model-based (MB) and deep learning based methods, have been explored to mitigate the artefacts associated with sparse-view acquisition. However, the explored methods formulated the reconstruction problem in a discrete framework, making it prone to measurement errors, discretization errors, and the extend of the ill-poseness of the problem scales with the discretization resolution. In this work, an implicit neural representation (INR) framework is proposed for image reconstruction in PACT with ring transducer arrays to address these issues. pecially, the initial heat distribution is represented as a continuous function of spatial coordinates using a multi-layer perceptron (MLP). The weights of the MLP are then determined by a training process in a self-supervised manner, by minimizing the errors between the measured and model predicted PA signals. After training, PA images can be mapped by feeding the coordinates to the network. Simulation and phantom experiments showed that the INR method performed best in preserving image fidelity and in artefacts suppression for the same acquisition condition, compared to universal back-projection and MB methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13696v1-abstract-full').style.display = 'none'; document.getElementById('2409.13696v1-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 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">arXiv admin note: substantial text overlap with arXiv:2406.17578</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.14156">arXiv:2408.14156</a> <span> [<a href="https://arxiv.org/pdf/2408.14156">pdf</a>, <a href="https://arxiv.org/format/2408.14156">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> </div> </div> <p class="title is-5 mathjax"> Integrated Sensing, Communication, and Powering over Multi-antenna OFDM Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yilong Chen</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+C">Chao Hu</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+Z">Zixiang Ren</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+H">Han Hu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Lei Liu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2408.14156v1-abstract-short" style="display: inline;"> This paper considers a multi-functional orthogonal frequency division multiplexing (OFDM) system with integrated sensing, communication, and powering (ISCAP), in which a multi-antenna base station (BS) transmits OFDM signals to simultaneously deliver information to multiple information receivers (IRs), provide energy supply to multiple energy receivers (ERs), and sense potential targets based on t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14156v1-abstract-full').style.display = 'inline'; document.getElementById('2408.14156v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.14156v1-abstract-full" style="display: none;"> This paper considers a multi-functional orthogonal frequency division multiplexing (OFDM) system with integrated sensing, communication, and powering (ISCAP), in which a multi-antenna base station (BS) transmits OFDM signals to simultaneously deliver information to multiple information receivers (IRs), provide energy supply to multiple energy receivers (ERs), and sense potential targets based on the echo signals. To facilitate ISCAP, the BS employs the joint transmit beamforming design by sending dedicated sensing/energy beams jointly with information beams. Furthermore, we consider the beam scanning for sensing, in which the joint beams scan in different directions over time to sense potential targets. In order to ensure the sensing beam scanning performance and meet the communication and powering requirements, it is essential to properly schedule IRs and ERs and design the resource allocation over time, frequency, and space. More specifically, we optimize the joint transmit beamforming over multiple OFDM symbols and subcarriers, with the objective of minimizing the average beampattern matching error of beam scanning for sensing, subject to the constraints on the average communication rates at IRs and the average harvested power at ERs. We find converged high-quality solutions to the formulated problem by proposing efficient iterative algorithms based on advanced optimization techniques. We also develop various heuristic designs based on the principles of zero-forcing (ZF) beamforming, round-robin user scheduling, and time switching, respectively. Numerical results show that our proposed algorithms adaptively generate information and sensing/energy beams at each time-frequency slot to match the scheduled IRs/ERs with the desired scanning beam, significantly outperforming the heuristic designs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.14156v1-abstract-full').style.display = 'none'; document.getElementById('2408.14156v1-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 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">13 pages, 12 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/2408.10067">arXiv:2408.10067</a> <span> [<a href="https://arxiv.org/pdf/2408.10067">pdf</a>, <a href="https://arxiv.org/format/2408.10067">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Towards a Benchmark for Colorectal Cancer Segmentation in Endorectal Ultrasound Videos: Dataset and Model Development </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jiang%2C+Y">Yuncheng Jiang</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Y">Yiwen Hu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zixun Zhang</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+J">Jun Wei</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+C">Chun-Mei Feng</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+X">Xuemei Tang</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+X">Xiang Wan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yong Liu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zhen 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="2408.10067v1-abstract-short" style="display: inline;"> Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders the development of automatic ultrasound diagnostics. In this paper, we collected and annotated the first benchmark dataset that covers diverse ERUS s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10067v1-abstract-full').style.display = 'inline'; document.getElementById('2408.10067v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.10067v1-abstract-full" style="display: none;"> Endorectal ultrasound (ERUS) is an important imaging modality that provides high reliability for diagnosing the depth and boundary of invasion in colorectal cancer. However, the lack of a large-scale ERUS dataset with high-quality annotations hinders the development of automatic ultrasound diagnostics. In this paper, we collected and annotated the first benchmark dataset that covers diverse ERUS scenarios, i.e. colorectal cancer segmentation, detection, and infiltration depth staging. Our ERUS-10K dataset comprises 77 videos and 10,000 high-resolution annotated frames. Based on this dataset, we further introduce a benchmark model for colorectal cancer segmentation, named the Adaptive Sparse-context TRansformer (ASTR). ASTR is designed based on three considerations: scanning mode discrepancy, temporal information, and low computational complexity. For generalizing to different scanning modes, the adaptive scanning-mode augmentation is proposed to convert between raw sector images and linear scan ones. For mining temporal information, the sparse-context transformer is incorporated to integrate inter-frame local and global features. For reducing computational complexity, the sparse-context block is introduced to extract contextual features from auxiliary frames. Finally, on the benchmark dataset, the proposed ASTR model achieves a 77.6% Dice score in rectal cancer segmentation, largely outperforming previous state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10067v1-abstract-full').style.display = 'none'; document.getElementById('2408.10067v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08602">arXiv:2408.08602</a> <span> [<a href="https://arxiv.org/pdf/2408.08602">pdf</a>, <a href="https://arxiv.org/format/2408.08602">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Discrete-time SIS Social Contagion Processes on Hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liang%2C+L">Lidan Liang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangzhou Liu</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="2408.08602v1-abstract-short" style="display: inline;"> Recent research on social contagion processes has revealed the limitations of traditional networks, which capture only pairwise relationships, to characterize complex multiparty relationships and group influences properly. Social contagion processes on higher-order networks (simplicial complexes and general hypergraphs) have therefore emerged as a novel frontier. In this work, we investigate discr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08602v1-abstract-full').style.display = 'inline'; document.getElementById('2408.08602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08602v1-abstract-full" style="display: none;"> Recent research on social contagion processes has revealed the limitations of traditional networks, which capture only pairwise relationships, to characterize complex multiparty relationships and group influences properly. Social contagion processes on higher-order networks (simplicial complexes and general hypergraphs) have therefore emerged as a novel frontier. In this work, we investigate discrete-time Susceptible-Infected-Susceptible (SIS) social contagion processes occurring on weighted and directed hypergraphs and their extensions to bivirus cases and general higher-order SIS processes with the aid of tensor algebra. Our focus lies in comprehensively characterizing the healthy state and endemic equilibria within this framework. The emergence of bistability or multistability behavior phenomena, where multiple equilibria coexist and are simultaneously locally asymptotically stable, is demonstrated in view of the presence of the higher-order interaction. The novel sufficient conditions of the appearance for system behaviors, which are determined by both (higher-order) network topology and transition rates, are provided to assess the likelihood of the SIS social contagion processes causing an outbreak. More importantly, given the equilibrium is locally stable, an explicit domain of attraction associated with the system parameters is constructed. Moreover, a learning method to estimate the transition rates is presented. In the end, the attained theoretical results are supplemented via numerical examples. Specifically, we evaluate the effectiveness of the networked SIS social contagion process by comparing it with the $2^n$-state Markov chain model. These numerical examples are given to highlight the performance of parameter learning algorithms and the system behaviors of the discrete-time SIS social contagion process. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08602v1-abstract-full').style.display = 'none'; document.getElementById('2408.08602v1-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.02047">arXiv:2408.02047</a> <span> [<a href="https://arxiv.org/pdf/2408.02047">pdf</a>, <a href="https://arxiv.org/format/2408.02047">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Y">Yinyu Wu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuhui Zhang</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+H">Huijun Xing</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yanyan Shen</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2408.02047v2-abstract-short" style="display: inline;"> Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02047v2-abstract-full').style.display = 'inline'; document.getElementById('2408.02047v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02047v2-abstract-full" style="display: none;"> Recently, the integration of mobile edge computing (MEC) and generative artificial intelligence (GAI) technology has given rise to a new area called mobile edge generation and computing (MEGC), which offers mobile users heterogeneous services such as task computing and content generation. In this letter, we investigate the joint communication, computation, and the AIGC resource allocation problem in an MEGC system. A latency minimization problem is first formulated to enhance the quality of service for mobile users. Due to the strong coupling of the optimization variables, we propose a new deep reinforcement learning-based algorithm to solve it efficiently. Numerical results demonstrate that the proposed algorithm can achieve lower latency than two baseline algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02047v2-abstract-full').style.display = 'none'; document.getElementById('2408.02047v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 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">5 pages, 6 figures. This paper has been accepted for publication by IEEE Networking Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.20746">arXiv:2405.20746</a> <span> [<a href="https://arxiv.org/pdf/2405.20746">pdf</a>, <a href="https://arxiv.org/ps/2405.20746">ps</a>, <a href="https://arxiv.org/format/2405.20746">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> </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/LWC.2024.3451246">10.1109/LWC.2024.3451246 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> UAV-Enabled Wireless Networks with Movable-Antenna Array: Flexible Beamforming and Trajectory Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+W">Wenchao Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuhui Zhang</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+H">Huijun Xing</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+Y">Yanyan Shen</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2405.20746v2-abstract-short" style="display: inline;"> Recently, movable antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems. In this letter, an unmanned aerial vehicle (UAV) enabled multi-user multi-input-single-output system enhanced by the MA array is investigated. To enhance the throughput capacity, we aim to maximize the achievable data rate by jointly optimizing the transmit… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20746v2-abstract-full').style.display = 'inline'; document.getElementById('2405.20746v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.20746v2-abstract-full" style="display: none;"> Recently, movable antenna (MA) array becomes a promising technology for improving the communication quality in wireless communication systems. In this letter, an unmanned aerial vehicle (UAV) enabled multi-user multi-input-single-output system enhanced by the MA array is investigated. To enhance the throughput capacity, we aim to maximize the achievable data rate by jointly optimizing the transmit beamforming, the UAV trajectory, and the positions of the MA array antennas. The formulated data rate maximization problem is a highly coupled non-convex problem, for which an alternating optimization based algorithm is proposed to get a sub-optimal solution. Numerical results have demonstrated the performance gain of the proposed method compared with conventional method with fixed-position antenna array. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.20746v2-abstract-full').style.display = 'none'; document.getElementById('2405.20746v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 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">This paper has been accepted for publication by IEEE Wireless Communications Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.18969">arXiv:2405.18969</a> <span> [<a href="https://arxiv.org/pdf/2405.18969">pdf</a>, <a href="https://arxiv.org/ps/2405.18969">ps</a>, <a href="https://arxiv.org/format/2405.18969">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> </div> </div> <p class="title is-5 mathjax"> Global and local observability of hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Chencheng Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Hao Yang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+B">Bin Jiang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2405.18969v1-abstract-short" style="display: inline;"> This paper studies observability for non-uniform hypergraphs with inputs and outputs. To capture higher-order interactions, we define a canonical non-homogeneous dynamical system with nonlinear outputs on hypergraphs. We then construct algebraic necessary and sufficient conditions based on polynomial ideals and varieties for global observability at an initial state of hypergraphs. An example is gi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18969v1-abstract-full').style.display = 'inline'; document.getElementById('2405.18969v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18969v1-abstract-full" style="display: none;"> This paper studies observability for non-uniform hypergraphs with inputs and outputs. To capture higher-order interactions, we define a canonical non-homogeneous dynamical system with nonlinear outputs on hypergraphs. We then construct algebraic necessary and sufficient conditions based on polynomial ideals and varieties for global observability at an initial state of hypergraphs. An example is given to illustrate the proposed criteria for observability. Further, necessary and sufficient conditions for local observability are derived based on rank conditions of observability matrices, which provide a framework to study local observability for non-uniform hypergraphs. Finally, the similarity of observability for hypergraphs is proposed using similarity of tensors, which reveals the relation of observability between two hypergraphs and helps to check the observability intuitively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18969v1-abstract-full').style.display = 'none'; document.getElementById('2405.18969v1-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> 29 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/2405.18333">arXiv:2405.18333</a> <span> [<a href="https://arxiv.org/pdf/2405.18333">pdf</a>, <a href="https://arxiv.org/format/2405.18333">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> </div> </div> <p class="title is-5 mathjax"> On the analysis of a higher-order Lotka-Volterra model: an application of S-tensors and the polynomial complementarity problem </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Q">Qi Zhao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+G">Guofeng Zhang</a>, <a href="/search/eess?searchtype=author&query=Jard%C3%B3n-Kojakhmetov%2C+H">Hildeberto Jard贸n-Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2405.18333v2-abstract-short" style="display: inline;"> It is known that the effect of species' density on species' growth is non-additive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce HOIs (Higher-Order Interactions) which are able to capture, for example, the indirect effect of one species on a second… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18333v2-abstract-full').style.display = 'inline'; document.getElementById('2405.18333v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18333v2-abstract-full" style="display: none;"> It is known that the effect of species' density on species' growth is non-additive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce HOIs (Higher-Order Interactions) which are able to capture, for example, the indirect effect of one species on a second one correlating to a third species. Towards this end, we propose a general higher-order Lotka-Volterra model. We provide an existence result of a positive equilibrium for a non-homogeneous polynomial equation system with the help of S-tensors. Afterward, by utilizing the latter result, as well as the theory of monotone systems and results from the polynomial complementarity problem, we provide comprehensive results regarding the existence, uniqueness, and stability of the corresponding equilibrium. These results can be regarded as natural extensions of many analogous ones for the classical Lotka-Volterra model, especially in the case of full cooperation, competition among two factions, and pure competition. Finally, illustrative numerical examples are provided to highlight our contributions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18333v2-abstract-full').style.display = 'none'; document.getElementById('2405.18333v2-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">v1</span> submitted 28 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/2405.04253">arXiv:2405.04253</a> <span> [<a href="https://arxiv.org/pdf/2405.04253">pdf</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> </div> </div> <p class="title is-5 mathjax"> Fermat Number Transform Based Chromatic Dispersion Compensation and Adaptive Equalization Algorithm </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+S">Siyu Chen</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Z">Zheli Liu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+W">Weihao Li</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Z">Zihe Hu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingming Zhang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Sheng Cui</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+M">Ming Tang</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="2405.04253v1-abstract-short" style="display: inline;"> By introducing the Fermat number transform into chromatic dispersion compensation and adaptive equalization, the computational complexity has been reduced by 68% compared with the con?ventional implementation. Experimental results validate its transmission performance with only 0.8 dB receiver sensitivity penalty in a 75 km-40 GBaud-PDM-16QAM system. </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.04253v1-abstract-full" style="display: none;"> By introducing the Fermat number transform into chromatic dispersion compensation and adaptive equalization, the computational complexity has been reduced by 68% compared with the con?ventional implementation. Experimental results validate its transmission performance with only 0.8 dB receiver sensitivity penalty in a 75 km-40 GBaud-PDM-16QAM system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.04253v1-abstract-full').style.display = 'none'; document.getElementById('2405.04253v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.00736">arXiv:2405.00736</a> <span> [<a href="https://arxiv.org/pdf/2405.00736">pdf</a>, <a href="https://arxiv.org/format/2405.00736">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Joint Signal Detection and Automatic Modulation Classification via Deep Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xing%2C+H">Huijun Xing</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xuhui Zhang</a>, <a href="/search/eess?searchtype=author&query=Chang%2C+S">Shuo Chang</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+J">Jinke Ren</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zixun Zhang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2405.00736v1-abstract-short" style="display: inline;"> Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different ca… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00736v1-abstract-full').style.display = 'inline'; document.getElementById('2405.00736v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.00736v1-abstract-full" style="display: none;"> Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different modulation schemes coexist at different carrier frequencies. We first generate a coexisting RADIOML dataset (CRML23) to facilitate the joint design. Different from the publicly available AMC dataset ignoring the signal detection step and containing only one signal, our synthetic dataset covers the more realistic multiple-signal coexisting scenario. Then, we present a joint framework for detection and classification (JDM) for such a multiple-signal coexisting environment, which consists of two modules for signal detection and AMC, respectively. In particular, these two modules are interconnected using a designated data structure called "proposal". Finally, we conduct extensive simulations over the newly developed dataset, which demonstrate the effectiveness of our designs. Our code and dataset are now available as open-source (https://github.com/Singingkettle/ChangShuoRadioData). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00736v1-abstract-full').style.display = 'none'; document.getElementById('2405.00736v1-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> 29 April, 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/2404.16152">arXiv:2404.16152</a> <span> [<a href="https://arxiv.org/pdf/2404.16152">pdf</a>, <a href="https://arxiv.org/ps/2404.16152">ps</a>, <a href="https://arxiv.org/format/2404.16152">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"> Rethinking Grant-Free Protocol in mMTC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+M">Minhao Zhu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yifei Sun</a>, <a href="/search/eess?searchtype=author&query=You%2C+L">Lizhao You</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaorui Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Ya-Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2404.16152v1-abstract-short" style="display: inline;"> This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.16152v1-abstract-full').style.display = 'inline'; document.getElementById('2404.16152v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.16152v1-abstract-full" style="display: none;"> This paper revisits the identity detection problem under the current grant-free protocol in massive machine-type communications (mMTC) by asking the following question: for stable identity detection performance, is it enough to permit active devices to transmit preambles without any handshaking with the base station (BS)? Specifically, in the current grant-free protocol, the BS blindly allocates a fixed length of preamble to devices for identity detection as it lacks the prior information on the number of active devices $K$. However, in practice, $K$ varies dynamically over time, resulting in degraded identity detection performance especially when $K$ is large. Consequently, the current grant-free protocol fails to ensure stable identity detection performance. To address this issue, we propose a two-stage communication protocol which consists of estimation of $K$ in Phase I and detection of identities of active devices in Phase II. The preamble length for identity detection in Phase II is dynamically allocated based on the estimated $K$ in Phase I through a table lookup manner such that the identity detection performance could always be better than a predefined threshold. In addition, we design an algorithm for estimating $K$ in Phase I, and exploit the estimated $K$ to reduce the computational complexity of the identity detector in Phase II. Numerical results demonstrate the effectiveness of the proposed two-stage communication protocol and algorithms. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.16152v1-abstract-full').style.display = 'none'; document.getElementById('2404.16152v1-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> 24 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">Submitted to 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/2403.17652">arXiv:2403.17652</a> <span> [<a href="https://arxiv.org/pdf/2403.17652">pdf</a>, <a href="https://arxiv.org/ps/2403.17652">ps</a>, <a href="https://arxiv.org/format/2403.17652">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"> Leveraging A Variety of Anchors in Cellular Network for Ubiquitous Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+L">Liang Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shuowen Zhang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2403.17652v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) has recently attracted tremendous attention from both academia and industry, being envisioned as a key part of the standards for the sixth-generation (6G) cellular network. A key challenge of 6G-oriented ISAC lies in how to perform ubiquitous sensing based on the communication signals and devices. Previous works have made great progresses on studying the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17652v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17652v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17652v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) has recently attracted tremendous attention from both academia and industry, being envisioned as a key part of the standards for the sixth-generation (6G) cellular network. A key challenge of 6G-oriented ISAC lies in how to perform ubiquitous sensing based on the communication signals and devices. Previous works have made great progresses on studying the signal waveform design that leads to optimal communication-sensing performance tradeoff. In this article, we aim to focus on issues arising from the exploitation of the communication devices for sensing in 6G network. Particularly, we will discuss about how to leverage various nodes available in the cellular network as anchors to perform ubiquitous sensing. On one hand, the base stations (BSs) will be the most important anchors in the future 6G ISAC network, since they can generate/process radio signals with high range/angle resolutions, and their positions are precisely known. Correspondingly, we will first study the BS-based sensing technique. On the other hand, the BSs alone may not enable ubiquitous sensing, since they cannot cover all the places with strong line-of-sight (LOS) links. This motivates us to investigate the possibility of using other nodes that are with higher density in the network to act as the anchors. Along this line, we are interested in two types of new anchors - user equipments (UEs) and reconfigurable intelligent surfaces (RISs). This paper will shed light on the opportunities and challenges brought by UE-assisted sensing and RIS-assisted sensing. Our goal is to devise a novel 6G-oriented sensing architecture where BSs, UEs, and RISs can work together to provide ubiquitous sensing services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17652v1-abstract-full').style.display = 'none'; document.getElementById('2403.17652v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">to appear in IEEE Communications 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/2403.16353">arXiv:2403.16353</a> <span> [<a href="https://arxiv.org/pdf/2403.16353">pdf</a>, <a href="https://arxiv.org/format/2403.16353">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"> Energy-Efficient Hybrid Beamforming with Dynamic On-off Control for Integrated Sensing, Communications, and Powering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hao%2C+Z">Zeyu Hao</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuan Fang</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Qiu%2C+L">Ling Qiu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2403.16353v1-abstract-short" style="display: inline;"> This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16353v1-abstract-full').style.display = 'inline'; document.getElementById('2403.16353v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.16353v1-abstract-full" style="display: none;"> This paper investigates the energy-efficient hybrid beamforming design for a multi-functional integrated sensing, communications, and powering (ISCAP) system. In this system, a base station (BS) with a hybrid analog-digital (HAD) architecture sends unified wireless signals to communicate with multiple information receivers (IRs), sense multiple point targets, and wirelessly charge multiple energy receivers (ERs) at the same time. To facilitate the energy-efficient design, we present a novel HAD architecture for the BS transmitter, which allows dynamic on-off control of its radio frequency (RF) chains and analog phase shifters (PSs) through a switch network. We also consider a practical and comprehensive power consumption model for the BS, by taking into account the power-dependent non-linear power amplifier (PA) efficiency, and the on-off non-transmission power consumption model of RF chains and PSs. We jointly design the hybrid beamforming and dynamic on-off control at the BS, aiming to minimize its total power consumption, while guaranteeing the performance requirements on communication rates, sensing Cram茅r-Rao bound (CRB), and harvested power levels. The formulation also takes into consideration the per-antenna transmit power constraint and the constant modulus constraints for the analog beamformer at the BS. The resulting optimization problem for ISCAP is highly non-convex. Please refer to the paper for a complete abstract. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.16353v1-abstract-full').style.display = 'none'; document.getElementById('2403.16353v1-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> 24 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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, submitted to IEEE Transactions on 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/2403.11809">arXiv:2403.11809</a> <span> [<a href="https://arxiv.org/pdf/2403.11809">pdf</a>, <a href="https://arxiv.org/format/2403.11809">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"> Sensing-Enhanced Channel Estimation for Near-Field XL-MIMO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+S">Shicong Liu</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+Z">Zhen Gao</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2403.11809v3-abstract-short" style="display: inline;"> Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11809v3-abstract-full').style.display = 'inline'; document.getElementById('2403.11809v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11809v3-abstract-full" style="display: none;"> Future sixth-generation (6G) systems are expected to leverage extremely large-scale multiple-input multiple-output (XL-MIMO) technology, which significantly expands the range of the near-field region. The spherical wavefront characteristics in the near field introduce additional degrees of freedom (DoFs), namely distance and angle, into the channel model, which leads to unique challenges in channel estimation (CE). In this paper, we propose a new sensing-enhanced uplink CE scheme for near-field XL-MIMO, which notably reduces the required quantity of baseband samples and the dictionary size. In particular, we first propose a sensing method that can be accomplished in a single time slot. It employs power sensors embedded within the antenna elements to measure the received power pattern rather than baseband samples. A time inversion algorithm is then proposed to precisely estimate the locations of users and scatterers, which offers a substantially lower computational complexity. Based on the estimated locations from sensing, a novel dictionary is then proposed by considering the eigen-problem based on the near-field transmission model, which facilitates efficient near-field CE with less baseband sampling and a more lightweight dictionary. Moreover, we derive the general form of the eigenvectors associated with the near-field channel matrix, revealing their noteworthy connection to the discrete prolate spheroidal sequence (DPSS). Simulation results unveil that the proposed time inversion algorithm achieves accurate localization with power measurements only, and remarkably outperforms various widely-adopted algorithms in terms of computational complexity. Furthermore, the proposed eigen-dictionary considerably improves the accuracy in CE with a compact dictionary size and a drastic reduction in baseband samples by up to 66%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11809v3-abstract-full').style.display = 'none'; document.getElementById('2403.11809v3-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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, 10 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/2403.03416">arXiv:2403.03416</a> <span> [<a href="https://arxiv.org/pdf/2403.03416">pdf</a>, <a href="https://arxiv.org/format/2403.03416">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> </div> </div> <p class="title is-5 mathjax"> On discrete-time polynomial dynamical systems on hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+G">Guofeng Zhang</a>, <a href="/search/eess?searchtype=author&query=Jard%C3%B3n-Kojakhmetov%2C+H">Hildeberto Jard贸n-Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2403.03416v2-abstract-short" style="display: inline;"> This paper studies the stability of discrete-time polynomial dynamical systems on hypergraphs by utilizing the Perron-Frobenius theorem for nonnegative tensors with respect to the tensors Z-eigenvalues and Z-eigenvectors. Firstly, for a multilinear polynomial system on a uniform hypergraph, we study the stability of the origin of the corresponding systems. Next, we extend our results to non-homoge… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03416v2-abstract-full').style.display = 'inline'; document.getElementById('2403.03416v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.03416v2-abstract-full" style="display: none;"> This paper studies the stability of discrete-time polynomial dynamical systems on hypergraphs by utilizing the Perron-Frobenius theorem for nonnegative tensors with respect to the tensors Z-eigenvalues and Z-eigenvectors. Firstly, for a multilinear polynomial system on a uniform hypergraph, we study the stability of the origin of the corresponding systems. Next, we extend our results to non-homogeneous polynomial systems on non-uniform hypergraphs. We confirm that the local stability of any discrete-time polynomial system is in general dominated by pairwise terms. Assuming that the origin is locally stable, we construct a conservative (but explicit) region of attraction from the system parameters. Finally, we validate our results via some numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.03416v2-abstract-full').style.display = 'none'; document.getElementById('2403.03416v2-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">arXiv admin note: text overlap with arXiv:2401.03652</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.02729">arXiv:2402.02729</a> <span> [<a href="https://arxiv.org/pdf/2402.02729">pdf</a>, <a href="https://arxiv.org/ps/2402.02729">ps</a>, <a href="https://arxiv.org/format/2402.02729">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="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="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zezhong Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Junting Chen</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2402.02729v1-abstract-short" style="display: inline;"> In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02729v1-abstract-full').style.display = 'inline'; document.getElementById('2402.02729v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.02729v1-abstract-full" style="display: none;"> In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation (CRME) approach enabled by the generative adversarial network (GAN), called as GAN-CRME, which features fast and accurate radio map estimation without the transmitters' information. The radio map is inferred by exploiting the interaction between distributed received signal strength (RSS) measurements at mobile users and the geographical map using a deep neural network estimator, resulting in low data-acquisition cost and computational complexity. Moreover, a GAN-based learning algorithm is proposed to boost the inference capability of the deep neural network estimator by exploiting the power of generative AI. Simulation results showcase that the proposed GAN-CRME is even capable of coarse error-correction when the geographical map information is inaccurate. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.02729v1-abstract-full').style.display = 'none'; document.getElementById('2402.02729v1-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 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/2401.13893">arXiv:2401.13893</a> <span> [<a href="https://arxiv.org/pdf/2401.13893">pdf</a>, <a href="https://arxiv.org/format/2401.13893">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> </div> </div> <p class="title is-5 mathjax"> A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Zhiyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yang Yang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+M">Mingzhe Chen</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+C">Caili Guo</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+J">Julian Cheng</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2401.13893v2-abstract-short" style="display: inline;"> The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13893v2-abstract-full').style.display = 'inline'; document.getElementById('2401.13893v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.13893v2-abstract-full" style="display: none;"> The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.13893v2-abstract-full').style.display = 'none'; document.getElementById('2401.13893v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 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/2401.03652">arXiv:2401.03652</a> <span> [<a href="https://arxiv.org/pdf/2401.03652">pdf</a>, <a href="https://arxiv.org/format/2401.03652">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> </div> </div> <p class="title is-5 mathjax"> On Metzler positive systems on hypergraphs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+G">Guofeng Zhang</a>, <a href="/search/eess?searchtype=author&query=Jard%C3%B3n-Kojakhmetov%2C+H">Hildeberto Jard贸n-Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2401.03652v3-abstract-short" style="display: inline;"> In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be uniform. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency tenso… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03652v3-abstract-full').style.display = 'inline'; document.getElementById('2401.03652v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03652v3-abstract-full" style="display: none;"> In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be uniform. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency tensor. This similarity enables us to extend existing tools of matrix analysis for studying dynamical systems evolving on graphs to the study of a class of polynomial dynamical systems evolving on hypergraphs utilizing the properties of tensors. To be more precise, in this paper, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors. Next, we focus on positive systems on hypergraphs associated with Metzler tensors. More importantly, we design control laws to stabilize the origin of this class of Metzler positive systems on hypergraphs. In the end, we apply our findings to two classic dynamical systems: a higher-order Lotka-Volterra population dynamics system and a higher-order SIS epidemic dynamic process. The corresponding novel stability results are accompanied by ample numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03652v3-abstract-full').style.display = 'none'; document.getElementById('2401.03652v3-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 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/2401.03516">arXiv:2401.03516</a> <span> [<a href="https://arxiv.org/pdf/2401.03516">pdf</a>, <a href="https://arxiv.org/ps/2401.03516">ps</a>, <a href="https://arxiv.org/format/2401.03516">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> </div> </div> <p class="title is-5 mathjax"> Integrated Sensing, Communication, and Powering (ISCAP): Towards Multi-functional 6G Wireless Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yilong Chen</a>, <a href="/search/eess?searchtype=author&query=Ren%2C+Z">Zixiang Ren</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+Y">Yong Zeng</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2401.03516v1-abstract-short" style="display: inline;"> This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and impr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03516v1-abstract-full').style.display = 'inline'; document.getElementById('2401.03516v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03516v1-abstract-full" style="display: none;"> This article presents a novel multi-functional system for a sixth-generation (6G) wireless network with integrated sensing, communication, and powering (ISCAP), which unifies integrated sensing and communication (ISAC) and wireless information and power transfer (WIPT) techniques. The multi-functional ISCAP network promises to enhance resource utilization efficiency, reduce network costs, and improve overall performance through versatile operational modes. Specifically, a multi-functional base station (BS) can enable multi-functional transmission, by exploiting the same radio signals to perform target/environment sensing, wireless communication, and wireless power transfer (WPT), simultaneously. Besides, the three functions can be intelligently coordinated to pursue mutual benefits,i.e., wireless sensing can be leveraged to enable light-training or even training-free WIPT by providing side-channel information, and the BS can utilize WPT to wirelessly charge low-power devices for ensuring sustainable ISAC. Furthermore, multiple multi-functional BSs can cooperate in both transmission and reception phases for efficient interference management, multi-static sensing, and distributed energy beamforming. For these operational modes, we discuss the technical challenges and potential solutions, particularly focusing on the fundamental performance tradeoff limits, transmission protocol design, as well as waveform and beamforming optimization. Finally, interesting research directions are identified. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03516v1-abstract-full').style.display = 'none'; document.getElementById('2401.03516v1-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 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">7 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.09028">arXiv:2311.09028</a> <span> [<a href="https://arxiv.org/pdf/2311.09028">pdf</a>, <a href="https://arxiv.org/format/2311.09028">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"> Integrating Sensing, Communication, and Power Transfer: Multiuser Beamforming Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhou%2C+Z">Ziqin Zhou</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xiaoyang Li</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+K">Kaibin Huang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2311.09028v1-abstract-short" style="display: inline;"> In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09028v1-abstract-full').style.display = 'inline'; document.getElementById('2311.09028v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.09028v1-abstract-full" style="display: none;"> In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.09028v1-abstract-full').style.display = 'none'; document.getElementById('2311.09028v1-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">This paper has been submitted to 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/2310.01036">arXiv:2310.01036</a> <span> [<a href="https://arxiv.org/pdf/2310.01036">pdf</a>, <a href="https://arxiv.org/format/2310.01036">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> </div> </div> <p class="title is-5 mathjax"> Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jiacheng Wang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xuemin Shen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</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="2310.01036v2-abstract-short" style="display: inline;"> As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.01036v2-abstract-full').style.display = 'inline'; document.getElementById('2310.01036v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.01036v2-abstract-full" style="display: none;"> As generative artificial intelligence (GAI) models continue to evolve, their generative capabilities are increasingly enhanced and being used extensively in content generation. Beyond this, GAI also excels in data modeling and analysis, benefitting wireless communication systems. In this article, we investigate applications of GAI in the physical layer and analyze its support for integrated sensing and communications (ISAC) systems. Specifically, we first provide an overview of GAI and ISAC, touching on GAI's potential support across multiple layers of ISAC. We then concentrate on the physical layer, investigating GAI's applications from various perspectives thoroughly, such as channel estimation, and demonstrate the value of these GAI-enhanced physical layer technologies for ISAC systems. In the case study, the proposed diffusion model-based method effectively estimates the signal direction of arrival under the near-field condition based on the uniform linear array, when antenna spacing surpassing half the wavelength. With a mean square error of 1.03 degrees, it confirms GAI's support for the physical layer in near-field sensing and communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.01036v2-abstract-full').style.display = 'none'; document.getElementById('2310.01036v2-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> 29 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.07460">arXiv:2309.07460</a> <span> [<a href="https://arxiv.org/pdf/2309.07460">pdf</a>, <a href="https://arxiv.org/format/2309.07460">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"> A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zeng%2C+Y">Yong Zeng</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Junting Chen</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+D">Di Wu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xiaoli Xu</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+S">Shi Jin</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+X">Xiqi Gao</a>, <a href="/search/eess?searchtype=author&query=Gesbert%2C+D">David Gesbert</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</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="2309.07460v2-abstract-short" style="display: inline;"> Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links beco… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07460v2-abstract-full').style.display = 'inline'; document.getElementById('2309.07460v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.07460v2-abstract-full" style="display: none;"> Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large antenna size, wide bandwidth, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, e.g., the estimated channels or beams between base station (BS) and user equipment (UE), making it possible to better learn the local wireless environment. By exploiting this new opportunity and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, followed by the comparison of CKM with various existing channel inference techniques. Next, the main techniques for CKM construction are discussed, including both environment model-free and environment model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.07460v2-abstract-full').style.display = 'none'; document.getElementById('2309.07460v2-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 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.05384">arXiv:2308.05384</a> <span> [<a href="https://arxiv.org/pdf/2308.05384">pdf</a>, <a href="https://arxiv.org/format/2308.05384">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="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Ruichen Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yinqiu Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jiacheng Wang</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+Y">Yijing Lin</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zonghang Li</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Kang%2C+J">Jiawen Kang</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+Z">Zehui Xiong</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+B">Bo Ai</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+H">Haibo Zhou</a>, <a href="/search/eess?searchtype=author&query=Kim%2C+D+I">Dong In Kim</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="2308.05384v2-abstract-short" style="display: inline;"> Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05384v2-abstract-full').style.display = 'inline'; document.getElementById('2308.05384v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.05384v2-abstract-full" style="display: none;"> Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across various applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This paper serves as a comprehensive tutorial on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains, such as vision, text, and audio generation. We detail how GDMs can be effectively harnessed to solve complex optimization problems inherent in networks. The paper first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), incentive mechanism design, Semantic Communications (SemCom), Internet of Vehicles (IoV) networks, etc. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design. We conclude with a discussion on potential future directions for GDM research and applications, providing major insights into how they can continue to shape the future of network optimization. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.05384v2-abstract-full').style.display = 'none'; document.getElementById('2308.05384v2-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">This paper has been accepted by IEEE Communications Surveys & Tutorials (COMST)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.07340">arXiv:2307.07340</a> <span> [<a href="https://arxiv.org/pdf/2307.07340">pdf</a>, <a href="https://arxiv.org/format/2307.07340">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"> A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhe Wang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jiayi Zhang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+H">Hongyang Du</a>, <a href="/search/eess?searchtype=author&query=Niyato%2C+D">Dusit Niyato</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+B">Bo Ai</a>, <a href="/search/eess?searchtype=author&query=Debbah%2C+M">M茅rouane Debbah</a>, <a href="/search/eess?searchtype=author&query=Letaief%2C+K+B">Khaled B. Letaief</a>, <a href="/search/eess?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</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="2307.07340v2-abstract-short" style="display: inline;"> Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless syst… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07340v2-abstract-full').style.display = 'inline'; document.getElementById('2307.07340v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.07340v2-abstract-full" style="display: none;"> Extremely large-scale multiple-input-multiple-output (XL-MIMO), which offers vast spatial degrees of freedom, has emerged as a potentially pivotal enabling technology for the sixth generation (6G) of wireless mobile networks. With its growing significance, both opportunities and challenges are concurrently manifesting. This paper presents a comprehensive survey of research on XL-MIMO wireless systems. In particular, we introduce four XL-MIMO hardware architectures: uniform linear array (ULA)-based XL-MIMO, uniform planar array (UPA)-based XL-MIMO utilizing either patch antennas or point antennas, and continuous aperture (CAP)-based XL-MIMO. We comprehensively analyze and discuss their characteristics and interrelationships. Following this, we introduce several electromagnetic characteristics and general distance boundaries in XL-MIMO. Given the distinct electromagnetic properties of near-field communications, we present a range of channel models to demonstrate the benefits of XL-MIMO. We further discuss and summarize signal processing schemes for XL-MIMO. It is worth noting that the low-complexity signal processing schemes and deep learning empowered signal processing schemes are reviewed and highlighted to promote the practical implementation of XL-MIMO. Furthermore, we explore the interplay between XL-MIMO and other emergent 6G technologies. Finally, we outline several compelling research directions for future XL-MIMO wireless communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.07340v2-abstract-full').style.display = 'none'; document.getElementById('2307.07340v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">44 pages, 10 figures, to appear in IEEE Communications Surveys & Tutorials</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2307.05357">arXiv:2307.05357</a> <span> [<a href="https://arxiv.org/pdf/2307.05357">pdf</a>, <a href="https://arxiv.org/format/2307.05357">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="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Over-the-Air Computation in OFDM Systems with Imperfect Channel State Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yilong Chen</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+H">Huijun Xing</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2307.05357v1-abstract-short" style="display: inline;"> This paper studies the over-the-air computation (AirComp) in an orthogonal frequency division multiplexing (OFDM) system with imperfect channel state information (CSI), in which multiple single-antenna wireless devices (WDs) simultaneously send uncoded signals to a multi-antenna access point (AP) for distributed functional computation over multiple subcarriers. In particular, we consider two scena… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.05357v1-abstract-full').style.display = 'inline'; document.getElementById('2307.05357v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.05357v1-abstract-full" style="display: none;"> This paper studies the over-the-air computation (AirComp) in an orthogonal frequency division multiplexing (OFDM) system with imperfect channel state information (CSI), in which multiple single-antenna wireless devices (WDs) simultaneously send uncoded signals to a multi-antenna access point (AP) for distributed functional computation over multiple subcarriers. In particular, we consider two scenarios with best-effort and error-constrained computation tasks, with the objectives of minimizing the average computation mean squared error (MSE) and the computation outage probability over the multiple subcarriers, respectively. Towards this end, we jointly optimize the transmit coefficients at the WDs and the receive beamforming vectors at the AP over subcarriers, subject to the maximum transmit power constraints at individual WDs. First, for the special case with a single receive antenna at the AP, we propose the semi-closed-form globally optimal solutions to the two problems using the Lagrange-duality method. It is shown that at each subcarrier, the WDs' optimized power control policy for average MSE minimization follows a regularized channel inversion structure, while that for computation outage probability minimization follows an on-off regularized channel inversion, with the regularization dependent on the transmit power budget and channel estimation error. Next, for the general case with multiple receive antennas at the AP, we present efficient algorithms based on alternating optimization and convex optimization to find converged solutions to both problems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.05357v1-abstract-full').style.display = 'none'; document.getElementById('2307.05357v1-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 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">13 pages, 6 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/2307.02242">arXiv:2307.02242</a> <span> [<a href="https://arxiv.org/pdf/2307.02242">pdf</a>, <a href="https://arxiv.org/format/2307.02242">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"> Multi-IRS-Enabled Integrated Sensing and Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuan Fang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Siyao Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xinmin Li</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+X">Xianghao Yu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2307.02242v2-abstract-short" style="display: inline;"> This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sens… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02242v2-abstract-full').style.display = 'inline'; document.getElementById('2307.02242v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2307.02242v2-abstract-full" style="display: none;"> This paper studies a multi-intelligent-reflecting-surface-(IRS)-enabled integrated sensing and communications (ISAC) system, in which multiple IRSs are installed to help the base station (BS) provide ISAC services at separate line-of-sight (LoS) blocked areas. We focus on the scenario with semi-passive uniform linear array (ULA) IRSs for sensing, in which each IRS is integrated with dedicated sensors for processing echo signals, and each IRS simultaneously serves one sensing target and multiple communication users (CUs) in its coverage area. In particular, we suppose that the BS sends combined information and dedicated sensing signals for ISAC. Two cases with point and extended targets are considered, in which each IRS aims to estimate the direction-of-arrival (DoA) of the corresponding target and the complete target response matrix, respectively. Under this setup, we first derive the closed-form Cram{茅}r-Rao bounds (CRBs) for parameters estimation under the two target models. For the point target case, the CRB for DoA estimation is shown to be inversely proportional to the cubic of the number of sensors at each IRS, while for the extended target case, the CRB for target response matrix estimation is proportional to the number of IRS sensors. Next, we consider two different types of CU receivers that can and cannot cancel the interference from dedicated sensing signals prior to information decoding. To achieve fair and optimized sensing performance, we minimize the maximum CRB at all IRSs for the two target cases, via jointly optimizing the transmit beamformers at the BS and the reflective beamformers at the multiple IRSs, subject to the minimum signal-to-interference-plus-noise ratio (SINR) constraints at individual CUs, the maximum transmit power constraint at the BS, and the unit-modulus constraints at the multiple IRSs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2307.02242v2-abstract-full').style.display = 'none'; document.getElementById('2307.02242v2-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.15303">arXiv:2306.15303</a> <span> [<a href="https://arxiv.org/pdf/2306.15303">pdf</a>, <a href="https://arxiv.org/format/2306.15303">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"> Energy-Efficient MIMO Integrated Sensing and Communications with On-off Non-transmission Power </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+G">Guanlin Wu</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuan Fang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2306.15303v1-abstract-short" style="display: inline;"> This paper investigates the energy efficiency of a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system, in which one multi-antenna base station (BS) transmits unified ISAC signals to a multi-antenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.15303v1-abstract-full').style.display = 'inline'; document.getElementById('2306.15303v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.15303v1-abstract-full" style="display: none;"> This paper investigates the energy efficiency of a multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) system, in which one multi-antenna base station (BS) transmits unified ISAC signals to a multi-antenna communication user (CU) and at the same time use the echo signals to estimate an extended target. We focus on one particular ISAC transmission block and take into account the practical on-off non-transmission power at the BS. Under this setup, we minimize the energy consumption at the BS while ensuring a minimum average data rate requirement for communication and a maximum Cram茅r-Rao bound (CRB) requirement for target estimation, by jointly optimizing the transmit covariance matrix and the ``on'' duration for active transmission. We obtain the optimal solution to the rate-and-CRB-constrained energy minimization problem in a semi-closed form. Interestingly, the obtained optimal solution is shown to unify the spectrum-efficient and energy-efficient communications and sensing designs. In particular, for the special MIMO sensing case with rate constraint inactive, the optimal solution follows the isotropic transmission with shortest ``on'' duration, in which the BS radiates the required sensing energy by using sufficiently high power over the shortest duration. For the general ISAC case, the optimal transmit covariance solution is of full rank and follows the eigenmode transmission based on the communication channel, while the optimal ``on'' duration is determined based on both the rate and CRB constraints. Numerical results show that the proposed ISAC design achieves significantly reduced energy consumption as compared to the benchmark schemes based on isotropic transmission, always-on transmission, and sensing or communications only designs, especially when the rate and CRB constraints become stringent. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.15303v1-abstract-full').style.display = 'none'; document.getElementById('2306.15303v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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">13 pages, 9 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/2306.02990">arXiv:2306.02990</a> <span> [<a href="https://arxiv.org/pdf/2306.02990">pdf</a>, <a href="https://arxiv.org/format/2306.02990">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="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"> Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tang%2C+Y">Yao Tang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+W">Wei Xu</a>, <a href="/search/eess?searchtype=author&query=Cheung%2C+M+H">Man Hon Cheung</a>, <a href="/search/eess?searchtype=author&query=Lok%2C+T">Tat-Ming Lok</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2306.02990v1-abstract-short" style="display: inline;"> Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onb… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.02990v1-abstract-full').style.display = 'inline'; document.getElementById('2306.02990v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.02990v1-abstract-full" style="display: none;"> Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. We first analyze the impact of UAV deployment on the sensing quality and identify a threshold value for the sensing elevation angle that guarantees a satisfactory quality of data samples. Due to the non-ideal sensing channels, we consider the probabilistic sensing model, where the successful sensing probability of each UAV is determined by its position. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. Theoretical results suggest that the convergence rate can be improved if UAVs have a uniform successful sensing probability. Based on this analysis, we formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we apply the alternating optimization technique, and propose the bandwidth, batch size, and position optimization (BBPO) scheme to optimize these three decision variables alternately. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.02990v1-abstract-full').style.display = 'none'; document.getElementById('2306.02990v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.00619">arXiv:2306.00619</a> <span> [<a href="https://arxiv.org/pdf/2306.00619">pdf</a>, <a href="https://arxiv.org/format/2306.00619">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> </div> </div> <p class="title is-5 mathjax"> General SIS diffusion process with indirect spreading pathways on a hypergraph </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangzhou Liu</a>, <a href="/search/eess?searchtype=author&query=Jard%C3%B3n-Kojakhmetov%2C+H">Hildeberto Jard贸n-Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2306.00619v3-abstract-short" style="display: inline;"> While conventional graphs only characterize pairwise interactions, higher-order networks (hypergraph, simplicial complex) capture multi-body interactions, which is a potentially more suitable modeling framework for a complex real system. However, the introduction of higher-order interactions brings new challenges for the rigorous analysis of such systems on a higher-order network. In this paper, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00619v3-abstract-full').style.display = 'inline'; document.getElementById('2306.00619v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.00619v3-abstract-full" style="display: none;"> While conventional graphs only characterize pairwise interactions, higher-order networks (hypergraph, simplicial complex) capture multi-body interactions, which is a potentially more suitable modeling framework for a complex real system. However, the introduction of higher-order interactions brings new challenges for the rigorous analysis of such systems on a higher-order network. In this paper, we study a series of SIS-type diffusion processes with both indirect and direct pathways on a directed hypergraph. In a concrete case, the model we propose is based on a specific choice (polynomial) of interaction function (how several agents influence each other when they are in a hyperedge). Then, by the same choice of interaction function, we further extend the system and propose a bi-virus competing model on a directed hypergraph by coupling two single-virus models together. Finally, the most general model in this paper considers an abstract interaction function under single-virus and bi-virus settings. For the single-virus model, we provide the results regarding healthy state and endemic equilibrium. For the bi-virus setting, we further give an analysis of the existence and stability of the healthy state, dominant endemic equilibria, and coexisting equilibria. All theoretical results are finally supported by some numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.00619v3-abstract-full').style.display = 'none'; document.getElementById('2306.00619v3-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> 9 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 05C65; 34D05; 34C12; 37N25; 92D30 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10055">arXiv:2305.10055</a> <span> [<a href="https://arxiv.org/pdf/2305.10055">pdf</a>, <a href="https://arxiv.org/format/2305.10055">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"> Optimized Joint Beamforming for Wireless Powered Over-the-Air Computation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Siyao Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xinmin Li</a>, <a href="/search/eess?searchtype=author&query=Long%2C+Y">Yin Long</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+J">Jie Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2305.10055v1-abstract-short" style="display: inline;"> This correspondence studies the wireless powered over-the-air computation (AirComp) for achieving sustainable wireless data aggregation (WDA) by integrating AirComp and wireless power transfer (WPT) into a joint design. In particular, we consider that a multi-antenna hybrid access point (HAP) employs the transmit energy beamforming to charge multiple single-antenna low-power wireless devices (WDs)… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10055v1-abstract-full').style.display = 'inline'; document.getElementById('2305.10055v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10055v1-abstract-full" style="display: none;"> This correspondence studies the wireless powered over-the-air computation (AirComp) for achieving sustainable wireless data aggregation (WDA) by integrating AirComp and wireless power transfer (WPT) into a joint design. In particular, we consider that a multi-antenna hybrid access point (HAP) employs the transmit energy beamforming to charge multiple single-antenna low-power wireless devices (WDs) in the downlink, and the WDs use the harvested energy to simultaneously send their messages to the HAP for AirComp in the uplink. Under this setup, we minimize the computation mean square error (MSE), by jointly optimizing the transmit energy beamforming and the receive AirComp beamforming at the HAP, as well as the transmit power at the WDs, subject to the maximum transmit power constraint at the HAP and the wireless energy harvesting constraints at individual WDs. To tackle the non-convex computation MSE minimization problem, we present an efficient algorithm to find a converged high-quality solution by using the alternating optimization technique. Numerical results show that the proposed joint WPT-AirComp approach significantly reduces the computation MSE, as compared to other benchmark schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10055v1-abstract-full').style.display = 'none'; document.getElementById('2305.10055v1-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 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">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/2304.05564">arXiv:2304.05564</a> <span> [<a href="https://arxiv.org/pdf/2304.05564">pdf</a>, <a href="https://arxiv.org/format/2304.05564">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="Image and Video Processing">eess.IV</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/10.1364/OE.485258">10.1364/OE.485258 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Neural Invertible Variable-degree Optical Aberrations Correction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuang Cui</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+B">Bingnan Wang</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+Q">Quan Zheng</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="2304.05564v1-abstract-short" style="display: inline;"> Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing. Though real-world optical aberrations va… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05564v1-abstract-full').style.display = 'inline'; document.getElementById('2304.05564v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.05564v1-abstract-full" style="display: none;"> Optical aberrations of optical systems cause significant degradation of imaging quality. Aberration correction by sophisticated lens designs and special glass materials generally incurs high cost of manufacturing and the increase in the weight of optical systems, thus recent work has shifted to aberration correction with deep learning-based post-processing. Though real-world optical aberrations vary in degree, existing methods cannot eliminate variable-degree aberrations well, especially for the severe degrees of degradation. Also, previous methods use a single feed-forward neural network and suffer from information loss in the output. To address the issues, we propose a novel aberration correction method with an invertible architecture by leveraging its information-lossless property. Within the architecture, we develop conditional invertible blocks to allow the processing of aberrations with variable degrees. Our method is evaluated on both a synthetic dataset from physics-based imaging simulation and a real captured dataset. Quantitative and qualitative experimental results demonstrate that our method outperforms compared methods in correcting variable-degree optical aberrations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05564v1-abstract-full').style.display = 'none'; document.getElementById('2304.05564v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Optics Express 2023 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.05119">arXiv:2304.05119</a> <span> [<a href="https://arxiv.org/pdf/2304.05119">pdf</a>, <a href="https://arxiv.org/format/2304.05119">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"> Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhaorui Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Ya-Feng Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Ziyue Wang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Liang Liu</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+H">Haoyuan Pan</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2304.05119v2-abstract-short" style="display: inline;"> This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05119v2-abstract-full').style.display = 'inline'; document.getElementById('2304.05119v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.05119v2-abstract-full" style="display: none;"> This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices $K$. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of $K$ is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for $K$ and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of $K$ are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of $K$ and the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.05119v2-abstract-full').style.display = 'none'; document.getElementById('2304.05119v2-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> 13 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 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">Submitted to 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/2301.05908">arXiv:2301.05908</a> <span> [<a href="https://arxiv.org/pdf/2301.05908">pdf</a>, <a href="https://arxiv.org/format/2301.05908">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey 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="Multimedia">cs.MM</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> An Order-Complexity Model for Aesthetic Quality Assessment of Symbolic Homophony Music Scores </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jin%2C+X">Xin Jin</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+W">Wu Zhou</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jinyu Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+D">Duo Xu</a>, <a href="/search/eess?searchtype=author&query=Rong%2C+Y">Yiqing Rong</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuai Cui</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="2301.05908v1-abstract-short" style="display: inline;"> Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05908v1-abstract-full').style.display = 'inline'; document.getElementById('2301.05908v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.05908v1-abstract-full" style="display: none;"> Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work are as follows: first, we put forward a homophony music score aesthetic model to objectively evaluate the quality of music score as a baseline model; second, we put forward eight basic music features and four music aesthetic features. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.05908v1-abstract-full').style.display = 'none'; document.getElementById('2301.05908v1-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2212.07608">arXiv:2212.07608</a> <span> [<a href="https://arxiv.org/pdf/2212.07608">pdf</a>, <a href="https://arxiv.org/format/2212.07608">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Output-Dependent Gaussian Process State-Space Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lin%2C+Z">Zhidi Lin</a>, <a href="/search/eess?searchtype=author&query=Cheng%2C+L">Lei Cheng</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+F">Feng Yin</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2212.07608v1-abstract-short" style="display: inline;"> Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.07608v1-abstract-full').style.display = 'inline'; document.getElementById('2212.07608v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.07608v1-abstract-full" style="display: none;"> Gaussian process state-space model (GPSSM) is a fully probabilistic state-space model that has attracted much attention over the past decade. However, the outputs of the transition function in the existing GPSSMs are assumed to be independent, meaning that the GPSSMs cannot exploit the inductive biases between different outputs and lose certain model capacities. To address this issue, this paper proposes an output-dependent and more realistic GPSSM by utilizing the well-known, simple yet practical linear model of coregionalization (LMC) framework to represent the output dependency. To jointly learn the output-dependent GPSSM and infer the latent states, we propose a variational sparse GP-based learning method that only gently increases the computational complexity. Experiments on both synthetic and real datasets demonstrate the superiority of the output-dependent GPSSM in terms of learning and inference performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.07608v1-abstract-full').style.display = 'none'; document.getElementById('2212.07608v1-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </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">5 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/2211.04258">arXiv:2211.04258</a> <span> [<a href="https://arxiv.org/pdf/2211.04258">pdf</a>, <a href="https://arxiv.org/format/2211.04258">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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> MetaLoc: Learning to Learn Wireless Localization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jun Gao</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+D">Dongze Wu</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+F">Feng Yin</a>, <a href="/search/eess?searchtype=author&query=Kong%2C+Q">Qinglei Kong</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+L">Lexi Xu</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2211.04258v5-abstract-short" style="display: inline;"> Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often struggle to generalize to new environments, which results in considerable time… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04258v5-abstract-full').style.display = 'inline'; document.getElementById('2211.04258v5-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.04258v5-abstract-full" style="display: none;"> Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on pure statistical signal processing or data-driven approaches, often struggle to generalize to new environments, which results in considerable time and effort being wasted. To address this challenge, we propose MetaLoc, which is the first fingerprinting-based localization framework that leverages the Model-Agnostic Meta-Learning (MAML). Specifically, built on a deep neural network with strong representation capabilities, MetaLoc is trained on historical data sourced from well-calibrated environments, employing a two-loop optimization mechanism to obtain the meta-parameters. These meta-parameters act as the initialization for quick adaptation in new environments, reducing the need for much human effort. The framework introduces two paradigms for the optimization of meta-parameters: a centralized paradigm that simplifies the process by sharing data from all historical environments, and a distributed paradigm that maintains data privacy by training meta-parameters for each specific environment separately. Furthermore, the advanced distributed paradigm modifies the vanilla MAML loss function to ensure that the reduction of loss occurs in a consistent direction across various training domains, thus facilitating faster convergence during training. Our experiments on both synthetic and real datasets demonstrate that MetaLoc outperforms baseline methods in terms of localization accuracy, robustness, and cost-effectiveness. The code and datasets used in this study are publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.04258v5-abstract-full').style.display = 'none'; document.getElementById('2211.04258v5-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> 29 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </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">to be published in IEEE JSAC (Special Issue: 5G/6G Precise Positioning on Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV))</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.07425">arXiv:2206.07425</a> <span> [<a href="https://arxiv.org/pdf/2206.07425">pdf</a>, <a href="https://arxiv.org/format/2206.07425">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> </div> </div> <p class="title is-5 mathjax"> Discrete-time Layered-network Epidemics Model with Time-varying Transition Rates and Multiple Resources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shaoxuan Cui</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fangzhou Liu</a>, <a href="/search/eess?searchtype=author&query=Jard%C3%B3n-Kojakhmetov%2C+H">Hildeberto Jard贸n-Kojakhmetov</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Ming Cao</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="2206.07425v4-abstract-short" style="display: inline;"> This paper studies a discrete-time time-varying multi-layer networked SIWS (susceptible-infected-water-susceptible) model with multiple resources under both single-virus and competing multi-virus settings. Besides the human-to-human interaction, we also consider that the disease can diffuse on different types of medium. We use \emph{resources} to refer to any media that the pathogen of a virus can… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07425v4-abstract-full').style.display = 'inline'; document.getElementById('2206.07425v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.07425v4-abstract-full" style="display: none;"> This paper studies a discrete-time time-varying multi-layer networked SIWS (susceptible-infected-water-susceptible) model with multiple resources under both single-virus and competing multi-virus settings. Besides the human-to-human interaction, we also consider that the disease can diffuse on different types of medium. We use \emph{resources} to refer to any media that the pathogen of a virus can spread through, and do not restrict the resource only to be water. In the single-virus case, we give a full analysis of the system's behaviour related to its healthy state and endemic equilibrium. In the multi-virus case, we show analytically that different equilibria appear driven by the competition among all viruses. We also show that some analytical results of the time-invariant system can be expanded into time-varying cases. Finally, we illustrate the results through some simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.07425v4-abstract-full').style.display = 'none'; document.getElementById('2206.07425v4-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 93A15 37N25 92F05 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.05949">arXiv:2206.05949</a> <span> [<a href="https://arxiv.org/pdf/2206.05949">pdf</a>, <a href="https://arxiv.org/format/2206.05949">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 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.2022.3226836">10.1109/JSTSP.2022.3226836 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+P">Peixi Liu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuai Wang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+W">Wei Jiang</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+W">Wu Luo</a>, <a href="/search/eess?searchtype=author&query=Poor%2C+H+V">H. Vincent Poor</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2206.05949v1-abstract-short" style="display: inline;"> In this paper, we address the problem of joint sensing, computation, and communication (SC$^{2}$) resource allocation for federated edge learning (FEEL) via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05949v1-abstract-full').style.display = 'inline'; document.getElementById('2206.05949v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.05949v1-abstract-full" style="display: none;"> In this paper, we address the problem of joint sensing, computation, and communication (SC$^{2}$) resource allocation for federated edge learning (FEEL) via a concrete case study of human motion recognition based on wireless sensing in ambient intelligence. First, by analyzing the wireless sensing process in human motion recognition, we find that there exists a thresholding value for the sensing transmit power, exceeding which yields sensing data samples with approximately the same satisfactory quality. Then, the joint SC$^{2}$ resource allocation problem is cast to maximize the convergence speed of FEEL, under the constraints on training time, energy supply, and sensing quality of each edge device. Solving this problem entails solving two subproblems in order: the first one reduces to determine the joint sensing and communication resource allocation that maximizes the total number of samples that can be sensed during the entire training process; the second one concerns the partition of the attained total number of sensed samples over all the communication rounds to determine the batch size at each round for convergence speed maximization. The first subproblem on joint sensing and communication resource allocation is converted to a single-variable optimization problem by exploiting the derived relation between different control variables (resources), which thus allows an efficient solution via one-dimensional grid search. For the second subproblem, it is found that the number of samples to be sensed (or batch size) at each round is a decreasing function of the loss function value attained at the round. Based on this relationship, the approximate optimal batch size at each communication round is derived in closed-form as a function of the round index. Finally, extensive simulation results are provided to validate the superiority of the proposed joint SC$^{2}$ resource allocation scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.05949v1-abstract-full').style.display = 'none'; document.getElementById('2206.05949v1-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> 13 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </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, submitted to 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/2206.00493">arXiv:2206.00493</a> <span> [<a href="https://arxiv.org/pdf/2206.00493">pdf</a>, <a href="https://arxiv.org/ps/2206.00493">ps</a>, <a href="https://arxiv.org/format/2206.00493">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"> Networked Sensing in 6G Cellular Networks: Opportunities and Challenges </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+L">Liang Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shuowen Zhang</a>, <a href="/search/eess?searchtype=author&query=Du%2C+R">Rui Du</a>, <a href="/search/eess?searchtype=author&query=Han%2C+T+X">Tong Xiao Han</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</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="2206.00493v1-abstract-short" style="display: inline;"> Radar and wireless communication are widely acknowledged as the two most successful applications of the radio technology over the past decades. Recently, there is a trend in both academia and industry to achieve integrated sensing and communication (ISAC) in one system via utilizing a common radio spectrum and the same hardware platform. This article will discuss about the possibility of exploitin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00493v1-abstract-full').style.display = 'inline'; document.getElementById('2206.00493v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.00493v1-abstract-full" style="display: none;"> Radar and wireless communication are widely acknowledged as the two most successful applications of the radio technology over the past decades. Recently, there is a trend in both academia and industry to achieve integrated sensing and communication (ISAC) in one system via utilizing a common radio spectrum and the same hardware platform. This article will discuss about the possibility of exploiting the future sixth-generation (6G) cellular network to realize ISAC. Our vision is that the cellular base stations (BSs) deployed all over the world can be transformed into a powerful sensor to provide highresolution localization services. Specifically, motivated by the joint encoding/decoding gain in multi-cell coordinated communication, we advocate the adoption of the networked sensing technique in 6G network to achieve the above goal, where the BSs can share the sensing information with each other for jointly estimating the locations and velocities of the targets. Several opportunities and challenges to realize networked sensing in the 6G era will be revealed in this article. Moreover, the future research directions for this promising trend will be outlined as well. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00493v1-abstract-full').style.display = 'none'; document.getElementById('2206.00493v1-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 June, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </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=Cui%2C+S&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Cui%2C+S&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> 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