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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01923">arXiv:2411.01923</a> <span> [<a href="https://arxiv.org/pdf/2411.01923">pdf</a>, <a href="https://arxiv.org/ps/2411.01923">ps</a>, <a href="https://arxiv.org/format/2411.01923">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"> User Activity Detection with Delay-Calibration for Asynchronous Massive Random Access </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shao%2C+Z">Zhichao Shao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=de+Lamare%2C+R+C">Rodrigo C. de Lamare</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yong 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="2411.01923v1-abstract-short" style="display: inline;"> This work considers an uplink asynchronous massive random access scenario in which a large number of users asynchronously access a base station equipped with multiple receive antennas. The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is performed. We propose a user activity detectio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01923v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01923v1-abstract-full" style="display: none;"> This work considers an uplink asynchronous massive random access scenario in which a large number of users asynchronously access a base station equipped with multiple receive antennas. The objective is to alleviate the problem of massive collision due to the limited number of orthogonal preambles of an access scheme in which user activity detection is performed. We propose a user activity detection with delay-calibration (UAD-DC) algorithm and investigate the benefits of oversampling for the estimation of continuous time delays at the receiver. The proposed algorithm iteratively estimates time delays and detects active users by noting that the collided users can be identified through accurate estimation of time delays. Due to the sporadic user activity patterns, the user activity detection problem can be formulated as a compressive sensing (CS) problem, which can be solved by a modified Turbo-CS algorithm under the consideration of correlated noise samples resulting from oversampling. A sliding-window technique is applied in the proposed algorithm to reduce the overall computational complexity. Moreover, we propose a new design of the pulse shaping filter by minimizing the Bayesian Cram茅r-Rao bound of the detection problem under the constraint of limited spectral bandwidth. Numerical results demonstrate the efficacy of the proposed algorithm in terms of the normalized mean squared error of the estimated channel, the probability of misdetection and the successful detection ratio. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01923v1-abstract-full').style.display = 'none'; document.getElementById('2411.01923v1-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">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">Submitted to IEEE Transactions on Vehicular Technology</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.23325">arXiv:2410.23325</a> <span> [<a href="https://arxiv.org/pdf/2410.23325">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</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="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> Transfer Learning in Vocal Education: Technical Evaluation of Limited Samples Describing Mezzo-soprano </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hou%2C+Z">Zhenyi Hou</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+X">Xu Zhao</a>, <a href="/search/eess?searchtype=author&query=Ye%2C+K">Kejie Ye</a>, <a href="/search/eess?searchtype=author&query=Sheng%2C+X">Xinyu Sheng</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+S">Shanggerile Jiang</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+J">Jiajing Xia</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yitao Zhang</a>, <a href="/search/eess?searchtype=author&query=Ban%2C+C">Chenxi Ban</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+D">Daijun Luo</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jiaxing Chen</a>, <a href="/search/eess?searchtype=author&query=Zou%2C+Y">Yan Zou</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Y">Yuchao Feng</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+G">Guangyu Fan</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2410.23325v1-abstract-short" style="display: inline;"> Vocal education in the music field is difficult to quantify due to the individual differences in singers' voices and the different quantitative criteria of singing techniques. Deep learning has great potential to be applied in music education due to its efficiency to handle complex data and perform quantitative analysis. However, accurate evaluations with limited samples over rare vocal types, suc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.23325v1-abstract-full').style.display = 'inline'; document.getElementById('2410.23325v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.23325v1-abstract-full" style="display: none;"> Vocal education in the music field is difficult to quantify due to the individual differences in singers' voices and the different quantitative criteria of singing techniques. Deep learning has great potential to be applied in music education due to its efficiency to handle complex data and perform quantitative analysis. However, accurate evaluations with limited samples over rare vocal types, such as Mezzo-soprano, requires extensive well-annotated data support using deep learning models. In order to attain the objective, we perform transfer learning by employing deep learning models pre-trained on the ImageNet and Urbansound8k datasets for the improvement on the precision of vocal technique evaluation. Furthermore, we tackle the problem of the lack of samples by constructing a dedicated dataset, the Mezzo-soprano Vocal Set (MVS), for vocal technique assessment. Our experimental results indicate that transfer learning increases the overall accuracy (OAcc) of all models by an average of 8.3%, with the highest accuracy at 94.2%. We not only provide a novel approach to evaluating Mezzo-soprano vocal techniques but also introduce a new quantitative assessment method for music education. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.23325v1-abstract-full').style.display = 'none'; document.getElementById('2410.23325v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01644">arXiv:2410.01644</a> <span> [<a href="https://arxiv.org/pdf/2410.01644">pdf</a>, <a href="https://arxiv.org/ps/2410.01644">ps</a>, <a href="https://arxiv.org/format/2410.01644">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+K">Kai Li</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+Y">Yilei Liang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wei Ni</a>, <a href="/search/eess?searchtype=author&query=Crowcroft%2C+J">Jon Crowcroft</a>, <a href="/search/eess?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/eess?searchtype=author&query=Akan%2C+O+B">Ozgur B. Akan</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="2410.01644v1-abstract-short" style="display: inline;"> This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01644v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01644v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01644v1-abstract-full" style="display: none;"> This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01644v1-abstract-full').style.display = 'none'; document.getElementById('2410.01644v1-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 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">5 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.17397">arXiv:2408.17397</a> <span> [<a href="https://arxiv.org/pdf/2408.17397">pdf</a>, <a href="https://arxiv.org/format/2408.17397">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"> End-to-End Learning for Task-Oriented Semantic Communications Over MIMO Channels: An Information-Theoretic Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cai%2C+C">Chang Cai</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y+A">Ying-Jun Angela 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="2408.17397v1-abstract-short" style="display: inline;"> This paper addresses the problem of end-to-end (E2E) design of learning and communication in a task-oriented semantic communication system. In particular, we consider a multi-device cooperative edge inference system over a wireless multiple-input multiple-output (MIMO) multiple access channel, where multiple devices transmit extracted features to a server to perform a classification task. We formu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.17397v1-abstract-full').style.display = 'inline'; document.getElementById('2408.17397v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.17397v1-abstract-full" style="display: none;"> This paper addresses the problem of end-to-end (E2E) design of learning and communication in a task-oriented semantic communication system. In particular, we consider a multi-device cooperative edge inference system over a wireless multiple-input multiple-output (MIMO) multiple access channel, where multiple devices transmit extracted features to a server to perform a classification task. We formulate the E2E design of feature encoding, MIMO precoding, and classification as a conditional mutual information maximization problem. However, it is notoriously difficult to design and train an E2E network that can be adaptive to both the task dataset and different channel realizations. Regarding network training, we propose a decoupled pretraining framework that separately trains the feature encoder and the MIMO precoder, with a maximum a posteriori (MAP) classifier employed at the server to generate the inference result. The feature encoder is pretrained exclusively using the task dataset, while the MIMO precoder is pretrained solely based on the channel and noise distributions. Nevertheless, we manage to align the pretraining objectives of each individual component with the E2E learning objective, so as to approach the performance bound of E2E learning. By leveraging the decoupled pretraining results for initialization, the E2E learning can be conducted with minimal training overhead. Regarding network architecture design, we develop two deep unfolded precoding networks that effectively incorporate the domain knowledge of the solution to the decoupled precoding problem. Simulation results on both the CIFAR-10 and ModelNet10 datasets verify that the proposed method achieves significantly higher classification accuracy compared to various baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.17397v1-abstract-full').style.display = 'none'; document.getElementById('2408.17397v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 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">major revision in 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/2406.17784">arXiv:2406.17784</a> <span> [<a href="https://arxiv.org/pdf/2406.17784">pdf</a>, <a href="https://arxiv.org/format/2406.17784">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"> Scalable Near-Field Localization Based on Partitioned Large-Scale Antenna Array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yuqing Zheng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingchen Zhang</a>, <a href="/search/eess?searchtype=author&query=Teng%2C+B">Boyu Teng</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+W">Wenjun Jiang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.17784v1-abstract-short" style="display: inline;"> This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assump… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17784v1-abstract-full').style.display = 'inline'; document.getElementById('2406.17784v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.17784v1-abstract-full" style="display: none;"> This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of localization accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.17784v1-abstract-full').style.display = 'none'; document.getElementById('2406.17784v1-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2312.12342</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.14934">arXiv:2406.14934</a> <span> [<a href="https://arxiv.org/pdf/2406.14934">pdf</a>, <a href="https://arxiv.org/format/2406.14934">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</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/10.1016/j.isatra.2024.05.010">10.1016/j.isatra.2024.05.010 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Learning Autonomous Race Driving with Action Mapping Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yuanda Wang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+C">Changyin Sun</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.14934v1-abstract-short" style="display: inline;"> Autonomous race driving poses a complex control challenge as vehicles must be operated at the edge of their handling limits to reduce lap times while respecting physical and safety constraints. This paper presents a novel reinforcement learning (RL)-based approach, incorporating the action mapping (AM) mechanism to manage state-dependent input constraints arising from limited tire-road friction. A… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14934v1-abstract-full').style.display = 'inline'; document.getElementById('2406.14934v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.14934v1-abstract-full" style="display: none;"> Autonomous race driving poses a complex control challenge as vehicles must be operated at the edge of their handling limits to reduce lap times while respecting physical and safety constraints. This paper presents a novel reinforcement learning (RL)-based approach, incorporating the action mapping (AM) mechanism to manage state-dependent input constraints arising from limited tire-road friction. A numerical approximation method is proposed to implement AM, addressing the complex dynamics associated with the friction constraints. The AM mechanism also allows the learned driving policy to be generalized to different friction conditions. Experimental results in our developed race simulator demonstrate that the proposed AM-RL approach achieves superior lap times and better success rates compared to the conventional RL-based approaches. The generalization capability of driving policy with AM is also validated in the experiments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.14934v1-abstract-full').style.display = 'none'; document.getElementById('2406.14934v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12703">arXiv:2406.12703</a> <span> [<a href="https://arxiv.org/pdf/2406.12703">pdf</a>, <a href="https://arxiv.org/format/2406.12703">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"> Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jincheng Yang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Lishun Wang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Miao Cao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Huan Wang</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yinping Zhao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.12703v1-abstract-short" style="display: inline;"> We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI), which captures a spatial-spectral data cube using snapshot 2D measurements and uses algorithms to reconstruct 3D hyperspectral images (HSI). However, current methods based on Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies and non-local similarities. The recently popular Transformer-b… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12703v1-abstract-full').style.display = 'inline'; document.getElementById('2406.12703v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12703v1-abstract-full" style="display: none;"> We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI), which captures a spatial-spectral data cube using snapshot 2D measurements and uses algorithms to reconstruct 3D hyperspectral images (HSI). However, current methods based on Convolutional Neural Networks (CNNs) struggle to capture long-range dependencies and non-local similarities. The recently popular Transformer-based methods are poorly deployed on downstream tasks due to the high computational cost caused by self-attention. In this paper, we propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN), applying deformable convolutional networks (DCN) to this task for the first time. Considering the sparsity of HSI, we design a deformable convolution module that exploits its deformability to capture long-range dependencies and non-local similarities. In addition, we propose a new spectral information interaction module that considers both coarse-grained and fine-grained spectral similarities. Extensive experiments demonstrate that our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12703v1-abstract-full').style.display = 'none'; document.getElementById('2406.12703v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 5 figures, Accepted by ICIP2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.12299">arXiv:2406.12299</a> <span> [<a href="https://arxiv.org/pdf/2406.12299">pdf</a>, <a href="https://arxiv.org/format/2406.12299">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Networking and Internet Architecture">cs.NI</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"> Exploiting and Securing ML Solutions in Near-RT RIC: A Perspective of an xApp </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dayaratne%2C+T">Thusitha Dayaratne</a>, <a href="/search/eess?searchtype=author&query=Vo%2C+V">Viet Vo</a>, <a href="/search/eess?searchtype=author&query=Lai%2C+S">Shangqi Lai</a>, <a href="/search/eess?searchtype=author&query=Abuadbba%2C+S">Sharif Abuadbba</a>, <a href="/search/eess?searchtype=author&query=Haydon%2C+B">Blake Haydon</a>, <a href="/search/eess?searchtype=author&query=Suzuki%2C+H">Hajime Suzuki</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xingliang Yuan</a>, <a href="/search/eess?searchtype=author&query=Rudolph%2C+C">Carsten Rudolph</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.12299v1-abstract-short" style="display: inline;"> Open Radio Access Networks (O-RAN) are emerging as a disruptive technology, revolutionising traditional mobile network architecture and deployments in the current 5G and the upcoming 6G era. Disaggregation of network architecture, inherent support for AI/ML workflows, cloud-native principles, scalability, and interoperability make O-RAN attractive to network providers for beyond-5G and 6G deployme… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12299v1-abstract-full').style.display = 'inline'; document.getElementById('2406.12299v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.12299v1-abstract-full" style="display: none;"> Open Radio Access Networks (O-RAN) are emerging as a disruptive technology, revolutionising traditional mobile network architecture and deployments in the current 5G and the upcoming 6G era. Disaggregation of network architecture, inherent support for AI/ML workflows, cloud-native principles, scalability, and interoperability make O-RAN attractive to network providers for beyond-5G and 6G deployments. Notably, the ability to deploy custom applications, including Machine Learning (ML) solutions as xApps or rApps on the RAN Intelligent Controllers (RICs), has immense potential for network function and resource optimisation. However, the openness, nascent standards, and distributed architecture of O-RAN and RICs introduce numerous vulnerabilities exploitable through multiple attack vectors, which have not yet been fully explored. To address this gap and ensure robust systems before large-scale deployments, this work analyses the security of ML-based applications deployed on the RIC platform. We focus on potential attacks, defence mechanisms, and pave the way for future research towards a more robust RIC platform. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.12299v1-abstract-full').style.display = 'none'; document.getElementById('2406.12299v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.08305">arXiv:2406.08305</a> <span> [<a href="https://arxiv.org/pdf/2406.08305">pdf</a>, <a href="https://arxiv.org/format/2406.08305">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"> Large Language Model(LLM) assisted End-to-End Network Health Management based on Multi-Scale Semanticization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tang%2C+F">Fengxiao Tang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xiaonan Wang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xun Yuan</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+L">Linfeng Luo</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+M">Ming Zhao</a>, <a href="/search/eess?searchtype=author&query=Kato%2C+N">Nei Kato</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.08305v1-abstract-short" style="display: inline;"> Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the dynamic heterogeneous networks (DHNs) environment. Moreover, current state-of-the-art distributed anomaly detection methods, which utilize specific machine learn… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08305v1-abstract-full').style.display = 'inline'; document.getElementById('2406.08305v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.08305v1-abstract-full" style="display: none;"> Network device and system health management is the foundation of modern network operations and maintenance. Traditional health management methods, relying on expert identification or simple rule-based algorithms, struggle to cope with the dynamic heterogeneous networks (DHNs) environment. Moreover, current state-of-the-art distributed anomaly detection methods, which utilize specific machine learning techniques, lack multi-scale adaptivity for heterogeneous device information, resulting in unsatisfactory diagnostic accuracy for DHNs. In this paper, we develop an LLM-assisted end-to-end intelligent network health management framework. The framework first proposes a Multi-Scale Semanticized Anomaly Detection Model (MSADM), incorporating semantic rule trees with an attention mechanism to address the multi-scale anomaly detection problem in DHNs. Secondly, a chain-of-thought-based large language model is embedded in downstream to adaptively analyze the fault detection results and produce an analysis report with detailed fault information and optimization strategies. Experimental results show that the accuracy of our proposed MSADM for heterogeneous network entity anomaly detection is as high as 91.31\%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.08305v1-abstract-full').style.display = 'none'; document.getElementById('2406.08305v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.06649">arXiv:2406.06649</a> <span> [<a href="https://arxiv.org/pdf/2406.06649">pdf</a>, <a href="https://arxiv.org/format/2406.06649">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> <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"> 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+K">Kai Liu</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+H">Haotong Qin</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Y">Yong Guo</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Kong%2C+L">Linghe Kong</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+G">Guihai Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yulun 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="2406.06649v1-abstract-short" style="display: inline;"> Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression and inference acceleration, respectively. However, it is notorious that low-bit quantization degrades the accuracy of SR models compared to their ful… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06649v1-abstract-full').style.display = 'inline'; document.getElementById('2406.06649v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.06649v1-abstract-full" style="display: none;"> Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression and inference acceleration, respectively. However, it is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. Despite several efforts to alleviate the degradation, the transformer-based SR model still suffers severe degradation due to its distinctive activation distribution. In this work, we present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization. The proposed method first investigates the weight and activation and finds that the distribution is characterized by coexisting symmetry and asymmetry, long tails. Specifically, we propose Distribution-Oriented Bound Initialization (DOBI), using different searching strategies to search a coarse bound for quantizers. To obtain refined quantizer parameters, we further propose Distillation Quantization Calibration (DQC), which employs a distillation approach to make the quantized model learn from its FP counterpart. Through extensive experiments on different bits and scaling factors, the performance of DOBI can reach the state-of-the-art (SOTA) while after stage two, our method surpasses existing PTQ in both metrics and visual effects. 2DQuant gains an increase in PSNR as high as 4.52dB on Set5 (x2) compared with SOTA when quantized to 2-bit and enjoys a 3.60x compression ratio and 5.08x speedup ratio. The code and models will be available at https://github.com/Kai-Liu001/2DQuant. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.06649v1-abstract-full').style.display = 'none'; document.getElementById('2406.06649v1-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 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 6 figures. The code and models will be available at https://github.com/Kai-Liu001/2DQuant</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.18167">arXiv:2405.18167</a> <span> [<a href="https://arxiv.org/pdf/2405.18167">pdf</a>, <a href="https://arxiv.org/format/2405.18167">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"> Confidence-aware multi-modality learning for eye disease screening </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zou%2C+K">Ke Zou</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+T">Tian Lin</a>, <a href="/search/eess?searchtype=author&query=Han%2C+Z">Zongbo Han</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+M">Meng Wang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xuedong Yuan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Haoyu Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+C">Changqing Zhang</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xiaojing Shen</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+H">Huazhu Fu</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="2405.18167v1-abstract-short" style="display: inline;"> Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18167v1-abstract-full').style.display = 'inline'; document.getElementById('2405.18167v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.18167v1-abstract-full" style="display: none;"> Multi-modal ophthalmic image classification plays a key role in diagnosing eye diseases, as it integrates information from different sources to complement their respective performances. However, recent improvements have mainly focused on accuracy, often neglecting the importance of confidence and robustness in predictions for diverse modalities. In this study, we propose a novel multi-modality evidential fusion pipeline for eye disease screening. It provides a measure of confidence for each modality and elegantly integrates the multi-modality information using a multi-distribution fusion perspective. Specifically, our method first utilizes normal inverse gamma prior distributions over pre-trained models to learn both aleatoric and epistemic uncertainty for uni-modality. Then, the normal inverse gamma distribution is analyzed as the Student's t distribution. Furthermore, within a confidence-aware fusion framework, we propose a mixture of Student's t distributions to effectively integrate different modalities, imparting the model with heavy-tailed properties and enhancing its robustness and reliability. More importantly, the confidence-aware multi-modality ranking regularization term induces the model to more reasonably rank the noisy single-modal and fused-modal confidence, leading to improved reliability and accuracy. Experimental results on both public and internal datasets demonstrate that our model excels in robustness, particularly in challenging scenarios involving Gaussian noise and modality missing conditions. Moreover, our model exhibits strong generalization capabilities to out-of-distribution data, underscoring its potential as a promising solution for multimodal eye disease screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.18167v1-abstract-full').style.display = 'none'; document.getElementById('2405.18167v1-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 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">27 pages, 7 figures, 9 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.11218">arXiv:2405.11218</a> <span> [<a href="https://arxiv.org/pdf/2405.11218">pdf</a>, <a href="https://arxiv.org/format/2405.11218">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"> Learning-based Block-wise Planar Channel Estimation for Time-Varying MIMO OFDM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chenchen Liu</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+W">Wenjun Jiang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</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="2405.11218v1-abstract-short" style="display: inline;"> In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we establish a block-wise planar channel model (BPCM) to characterize the correlation of the channel across su… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11218v1-abstract-full').style.display = 'inline'; document.getElementById('2405.11218v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.11218v1-abstract-full" style="display: none;"> In this paper, we propose a learning-based block-wise planar channel estimator (LBPCE) with high accuracy and low complexity to estimate the time-varying frequency-selective channel of a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system. First, we establish a block-wise planar channel model (BPCM) to characterize the correlation of the channel across subcarriers and OFDM symbols. Specifically, adjacent subcarriers and OFDM symbols are divided into several sub-blocks, and an affine function (i.e., a plane) with only three variables (namely, mean, time-domain slope, and frequency-domain slope) is used to approximate the channel in each sub-block, which significantly reduces the number of variables to be determined in channel estimation. Second, we design a 3D dilated residual convolutional network (3D-DRCN) that leverages the time-frequency-space-domain correlations of the channel to further improve the channel estimates of each user. Numerical results demonstrate that the proposed significantly outperforms the state-of-the-art estimators and maintains a relatively low computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.11218v1-abstract-full').style.display = 'none'; document.getElementById('2405.11218v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 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/2405.00135">arXiv:2405.00135</a> <span> [<a href="https://arxiv.org/pdf/2405.00135">pdf</a>, <a href="https://arxiv.org/format/2405.00135">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"> Improving Channel Resilience for Task-Oriented Semantic Communications: A Unified Information Bottleneck Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lyu%2C+S">Shuai Lyu</a>, <a href="/search/eess?searchtype=author&query=Sun%2C+Y">Yao Sun</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+L">Linke Guo</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaoyong Yuan</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+F">Fang Fang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+L">Lan Zhang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xianbin Wang</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="2405.00135v1-abstract-short" style="display: inline;"> Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inherent semantic distinctions among encoded features. Due to unavoidable channel variations from time and frequency-selective fading, semantically sensitive feature units could be more susceptible to erroneous inference if… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00135v1-abstract-full').style.display = 'inline'; document.getElementById('2405.00135v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.00135v1-abstract-full" style="display: none;"> Task-oriented semantic communications (TSC) enhance radio resource efficiency by transmitting task-relevant semantic information. However, current research often overlooks the inherent semantic distinctions among encoded features. Due to unavoidable channel variations from time and frequency-selective fading, semantically sensitive feature units could be more susceptible to erroneous inference if corrupted by dynamic channels. Therefore, this letter introduces a unified channel-resilient TSC framework via information bottleneck. This framework complements existing TSC approaches by controlling information flow to capture fine-grained feature-level semantic robustness. Experiments on a case study for real-time subchannel allocation validate the framework's effectiveness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00135v1-abstract-full').style.display = 'none'; document.getElementById('2405.00135v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 April, 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 work has been submitted to the IEEE 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/2404.02159">arXiv:2404.02159</a> <span> [<a href="https://arxiv.org/pdf/2404.02159">pdf</a>, <a href="https://arxiv.org/format/2404.02159">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"> Fairness-aware Age-of-Information Minimization in WPT-Assisted Short-Packet THz Communications for mURLLC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+Y">Yao Zhu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaopeng Yuan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Y">Yulin Hu</a>, <a href="/search/eess?searchtype=author&query=Ai%2C+B">Bo Ai</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+R">Ruikang Wang</a>, <a href="/search/eess?searchtype=author&query=Han%2C+B">Bin Han</a>, <a href="/search/eess?searchtype=author&query=Schmeink%2C+A">Anke Schmeink</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="2404.02159v1-abstract-short" style="display: inline;"> The technological landscape is swiftly advancing towards large-scale systems, creating significant opportunities, particularly in the domain of Terahertz (THz) communications. Networks designed for massive connectivity, comprising numerous Internet of Things (IoT) devices, are at the forefront of this advancement. In this paper, we consider Wireless Power Transfer (WPT)-enabled networks that suppo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02159v1-abstract-full').style.display = 'inline'; document.getElementById('2404.02159v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.02159v1-abstract-full" style="display: none;"> The technological landscape is swiftly advancing towards large-scale systems, creating significant opportunities, particularly in the domain of Terahertz (THz) communications. Networks designed for massive connectivity, comprising numerous Internet of Things (IoT) devices, are at the forefront of this advancement. In this paper, we consider Wireless Power Transfer (WPT)-enabled networks that support these IoT devices with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services.The focus of such networks is information freshness, with the Age-of-Information (AoI) serving as the pivotal performance metric. In particular, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings establish the convexity property of the problem, which can be solved efficiently. Furthermore, we introduce the concept of AoI-oriented cluster capacity, examining the relationship between the number of supported devices and the AoI performance in the network. Numerical simulations validate the advantage of our proposed approach in enhancing AoI performance, indicating its potential to guide the design of future THz communication systems for IoT applications requiring mURLLC services. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02159v1-abstract-full').style.display = 'none'; document.getElementById('2404.02159v1-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 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.20018">arXiv:2403.20018</a> <span> [<a href="https://arxiv.org/pdf/2403.20018">pdf</a>, <a href="https://arxiv.org/format/2403.20018">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"> SCINeRF: Neural Radiance Fields from a Snapshot Compressive Image </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yunhao Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xiaodong Wang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+P">Ping Wang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+P">Peidong 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="2403.20018v1-abstract-short" style="display: inline;"> In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20018v1-abstract-full').style.display = 'inline'; document.getElementById('2403.20018v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.20018v1-abstract-full" style="display: none;"> In this paper, we explore the potential of Snapshot Compressive Imaging (SCI) technique for recovering the underlying 3D scene representation from a single temporal compressed image. SCI is a cost-effective method that enables the recording of high-dimensional data, such as hyperspectral or temporal information, into a single image using low-cost 2D imaging sensors. To achieve this, a series of specially designed 2D masks are usually employed, which not only reduces storage requirements but also offers potential privacy protection. Inspired by this, to take one step further, our approach builds upon the powerful 3D scene representation capabilities of neural radiance fields (NeRF). Specifically, we formulate the physical imaging process of SCI as part of the training of NeRF, allowing us to exploit its impressive performance in capturing complex scene structures. To assess the effectiveness of our method, we conduct extensive evaluations using both synthetic data and real data captured by our SCI system. Extensive experimental results demonstrate that our proposed approach surpasses the state-of-the-art methods in terms of image reconstruction and novel view image synthesis. Moreover, our method also exhibits the ability to restore high frame-rate multi-view consistent images by leveraging SCI and the rendering capabilities of NeRF. The code is available at https://github.com/WU-CVGL/SCINeRF. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.20018v1-abstract-full').style.display = 'none'; document.getElementById('2403.20018v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.19944">arXiv:2403.19944</a> <span> [<a href="https://arxiv.org/pdf/2403.19944">pdf</a>, <a href="https://arxiv.org/format/2403.19944">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> <p class="title is-5 mathjax"> Binarized Low-light Raw Video Enhancement </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+G">Gengchen Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yulun Zhang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+Y">Ying Fu</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="2403.19944v1-abstract-short" style="display: inline;"> Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19944v1-abstract-full').style.display = 'inline'; document.getElementById('2403.19944v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.19944v1-abstract-full" style="display: none;"> Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory costs, which hinder their applications on resource-limited devices. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless, there are two main issues with binarizing video enhancement models. One is how to fuse the temporal information to improve low-light denoising without complex modules. The other is how to narrow the performance gap between binary convolutions with the full precision ones. To address the first issue, we introduce a spatial-temporal shift operation, which is easy-to-binarize and effective. The temporal shift efficiently aggregates the features of neighbor frames and the spatial shift handles the misalignment caused by the large motion in videos. For the second issue, we present a distribution-aware binary convolution, which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized low-light raw video enhancement method can attain a promising performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.19944v1-abstract-full').style.display = 'none'; document.getElementById('2403.19944v1-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 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">Accepted at CVPR 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.06653">arXiv:2403.06653</a> <span> [<a href="https://arxiv.org/pdf/2403.06653">pdf</a>, <a href="https://arxiv.org/format/2403.06653">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"> UAV-Enabled Asynchronous Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhai%2C+Z">Zhiyuan Zhai</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Huiyuan Yang</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="2403.06653v1-abstract-short" style="display: inline;"> To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). However, there are some critical challenges for FL deployment. One major challenge called straggler issue severely limits FL's coverage where the device with the weakest channel condition becomes the bottleneck o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06653v1-abstract-full').style.display = 'inline'; document.getElementById('2403.06653v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.06653v1-abstract-full" style="display: none;"> To exploit unprecedented data generation in mobile edge networks, federated learning (FL) has emerged as a promising alternative to the conventional centralized machine learning (ML). However, there are some critical challenges for FL deployment. One major challenge called straggler issue severely limits FL's coverage where the device with the weakest channel condition becomes the bottleneck of the model aggregation performance. Besides, the huge uplink communication overhead compromises the effectiveness of FL, which is particularly pronounced in large-scale systems. To address the straggler issue, we propose the integration of an unmanned aerial vehicle (UAV) as the parameter server (UAV-PS) to coordinate the FL implementation. We further employ over-the-air computation technique that leverages the superposition property of wireless channels for efficient uplink communication. Specifically, in this paper, we develop a novel UAV-enabled over-the-air asynchronous FL (UAV-AFL) framework which supports the UAV-PS in updating the model continuously to enhance the learning performance. Moreover, we conduct a convergence analysis to quantitatively capture the impact of model asynchrony, device selection and communication errors on the UAV-AFL learning performance. Based on this, a unified communication-learning problem is formulated to maximize asymptotical learning performance by optimizing the UAV-PS trajectory, device selection and over-the-air transceiver design. Simulation results demonstrate that the proposed scheme achieves substantially learning efficiency improvement compared with the state-of-the-art approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.06653v1-abstract-full').style.display = 'none'; document.getElementById('2403.06653v1-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 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.02307">arXiv:2403.02307</a> <span> [<a href="https://arxiv.org/pdf/2403.02307">pdf</a>, <a href="https://arxiv.org/format/2403.02307">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"> Harnessing Intra-group Variations Via a Population-Level Context for Pathology Detection </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Githinji%2C+P+B">P. Bilha Githinji</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xi Yuan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhenglin Chen</a>, <a href="/search/eess?searchtype=author&query=Gul%2C+I">Ijaz Gul</a>, <a href="/search/eess?searchtype=author&query=Shang%2C+D">Dingqi Shang</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+W">Wen Liang</a>, <a href="/search/eess?searchtype=author&query=Deng%2C+J">Jianming Deng</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+D">Dan Zeng</a>, <a href="/search/eess?searchtype=author&query=yu%2C+D">Dongmei yu</a>, <a href="/search/eess?searchtype=author&query=Yan%2C+C">Chenggang Yan</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+P">Peiwu Qin</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="2403.02307v2-abstract-short" style="display: inline;"> Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pathology detection and employs a graph th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02307v2-abstract-full').style.display = 'inline'; document.getElementById('2403.02307v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.02307v2-abstract-full" style="display: none;"> Realizing sufficient separability between the distributions of healthy and pathological samples is a critical obstacle for pathology detection convolutional models. Moreover, these models exhibit a bias for contrast-based images, with diminished performance on texture-based medical images. This study introduces the notion of a population-level context for pathology detection and employs a graph theoretic approach to model and incorporate it into the latent code of an autoencoder via a refinement module we term PopuSense. PopuSense seeks to capture additional intra-group variations inherent in biomedical data that a local or global context of the convolutional model might miss or smooth out. Proof-of-concept experiments on contrast-based and texture-based images, with minimal adaptation, encounter the existing preference for intensity-based input. Nevertheless, PopuSense demonstrates improved separability in contrast-based images, presenting an additional avenue for refining representations learned by a model. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.02307v2-abstract-full').style.display = 'none'; document.getElementById('2403.02307v2-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.13628">arXiv:2402.13628</a> <span> [<a href="https://arxiv.org/pdf/2402.13628">pdf</a>, <a href="https://arxiv.org/format/2402.13628">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhao%2C+D">Dafang Zhao</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zheng Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zhengmao Li</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaolei Yuan</a>, <a href="/search/eess?searchtype=author&query=Taniguchi%2C+I">Ittetsu Taniguchi</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="2402.13628v1-abstract-short" style="display: inline;"> Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. However, for large-scale HVAC system managem… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13628v1-abstract-full').style.display = 'inline'; document.getElementById('2402.13628v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.13628v1-abstract-full" style="display: none;"> Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. However, for large-scale HVAC system management, it is difficult to construct a detailed model for each subsystem. In this paper, a new data-driven room temperature prediction model is proposed based on the k-means clustering method. The proposed data-driven temperature prediction approach extracts the system operation feature through historical data analysis and further simplifies the system-level model to improve generalization and computational efficiency. We evaluate the proposed approach in the real world. The results demonstrated that our approach can significantly reduce modeling time without reducing prediction accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.13628v1-abstract-full').style.display = 'none'; document.getElementById('2402.13628v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted and will be published on IEEE PES GM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.04448">arXiv:2402.04448</a> <span> [<a href="https://arxiv.org/pdf/2402.04448">pdf</a>, <a href="https://arxiv.org/format/2402.04448">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"> Failure Analysis in Next-Generation Critical Cellular Communication Infrastructures </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Bi%2C+S">Siguo Bi</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+S">Shuyan Hu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+K">Kai Li</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wei Ni</a>, <a href="/search/eess?searchtype=author&query=Hossain%2C+E">Ekram Hossain</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</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="2402.04448v1-abstract-short" style="display: inline;"> The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04448v1-abstract-full').style.display = 'inline'; document.getElementById('2402.04448v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.04448v1-abstract-full" style="display: none;"> The advent of communication technologies marks a transformative phase in critical infrastructure construction, where the meticulous analysis of failures becomes paramount in achieving the fundamental objectives of continuity, security, and availability. This survey enriches the discourse on failures, failure analysis, and countermeasures in the context of the next-generation critical communication infrastructures. Through an exhaustive examination of existing literature, we discern and categorize prominent research orientations with focuses on, namely resource depletion, security vulnerabilities, and system availability concerns. We also analyze constructive countermeasures tailored to address identified failure scenarios and their prevention. Furthermore, the survey emphasizes the imperative for standardization in addressing failures related to Artificial Intelligence (AI) within the ambit of the sixth-generation (6G) networks, accounting for the forward-looking perspective for the envisioned intelligence of 6G network architecture. By identifying new challenges and delineating future research directions, this survey can help guide stakeholders toward unexplored territories, fostering innovation and resilience in critical communication infrastructure development and failure prevention. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.04448v1-abstract-full').style.display = 'none'; document.getElementById('2402.04448v1-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">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/2401.05394">arXiv:2401.05394</a> <span> [<a href="https://arxiv.org/pdf/2401.05394">pdf</a>, <a href="https://arxiv.org/format/2401.05394">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=de+Vazelhes%2C+W">William de Vazelhes</a>, <a href="/search/eess?searchtype=author&query=Mukhoty%2C+B">Bhaskar Mukhoty</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiao-Tong Yuan</a>, <a href="/search/eess?searchtype=author&query=Gu%2C+B">Bin Gu</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="2401.05394v4-abstract-short" style="display: inline;"> Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through ea… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.05394v4-abstract-full').style.display = 'inline'; document.getElementById('2401.05394v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.05394v4-abstract-full" style="display: none;"> Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through early stopping, rather than the tedious grid-search used in the traditional methods. However, most of those iterative methods are based on the $\ell_1$ norm which requires restrictive applicability conditions and could fail in many cases. Therefore, achieving sparse recovery with iterative regularization methods under a wider range of conditions has yet to be further explored. To address this issue, we propose a novel iterative regularization algorithm, IRKSN, based on the $k$-support norm regularizer rather than the $\ell_1$ norm. We provide conditions for sparse recovery with IRKSN, and compare them with traditional conditions for recovery with $\ell_1$ norm regularizers. Additionally, we give an early stopping bound on the model error of IRKSN with explicit constants, achieving the standard linear rate for sparse recovery. Finally, we illustrate the applicability of our algorithm on several experiments, including a support recovery experiment with a correlated design matrix. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.05394v4-abstract-full').style.display = 'none'; document.getElementById('2401.05394v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2023; <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">Accepted at AAAI 2024. Code at https://github.com/wdevazelhes/IRKSN_AAAI2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.03626">arXiv:2401.03626</a> <span> [<a href="https://arxiv.org/pdf/2401.03626">pdf</a>, <a href="https://arxiv.org/format/2401.03626">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"> Hybrid Vector Message Passing for Generalized Bilinear Factorization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Jiang%2C+H">Hao Jiang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Q">Qinghua Guo</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="2401.03626v1-abstract-short" style="display: inline;"> In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/m… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03626v1-abstract-full').style.display = 'inline'; document.getElementById('2401.03626v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03626v1-abstract-full" style="display: none;"> In this paper, we propose a new message passing algorithm that utilizes hybrid vector message passing (HVMP) to solve the generalized bilinear factorization (GBF) problem. The proposed GBF-HVMP algorithm integrates expectation propagation (EP) and variational message passing (VMP) via variational free energy minimization, yielding tractable Gaussian messages. Furthermore, GBF-HVMP enables vector/matrix variables rather than scalar ones in message passing, resulting in a loop-free Bayesian network that improves convergence. Numerical results show that GBF-HVMP significantly outperforms state-of-the-art methods in terms of NMSE performance and computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03626v1-abstract-full').style.display = 'none'; document.getElementById('2401.03626v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.12342">arXiv:2312.12342</a> <span> [<a href="https://arxiv.org/pdf/2312.12342">pdf</a>, <a href="https://arxiv.org/format/2312.12342">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"> Scalable Near-Field Localization Based on Partitioned Large-Scale Antenna Array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yuqing Zheng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Mingchen Zhang</a>, <a href="/search/eess?searchtype=author&query=Teng%2C+B">Boyu Teng</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+W">Wenjun Jiang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.12342v2-abstract-short" style="display: inline;"> This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assump… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.12342v2-abstract-full').style.display = 'inline'; document.getElementById('2312.12342v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.12342v2-abstract-full" style="display: none;"> This paper studies a passive localization system, where an extremely large-scale antenna array (ELAA) is deployed at the base station (BS) to locate a user equipment (UE) residing in its near-field (Fresnel) region. We propose a novel algorithm, named array partitioning-based location estimation (APLE), for scalable near-field localization. The APLE algorithm is developed based on the basic assumption that, by partitioning the ELAA into multiple subarrays, the UE can be approximated as in the far-field region of each subarray. We establish a Bayeian inference framework based on the geometric constraints between the UE location and the angles of arrivals (AoAs) at different subarrays. Then, the APLE algorithm is designed based on the message-passing principle for the localization of the UE. APLE exhibits linear computational complexity with the number of BS antennas, leading to a significant reduction in complexity compared to existing methods. We further propose an enhanced APLE (E-APLE) algorithm that refines the location estimate obtained from APLE by following the maximum likelihood principle. The E-APLE algorithm achieves superior localization accuracy compared to APLE while maintaining a linear complexity with the number of BS antennas. Numerical results demonstrate that the proposed APLE and E-APLE algorithms outperform the existing baselines in terms of localization accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.12342v2-abstract-full').style.display = 'none'; document.getElementById('2312.12342v2-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.01336">arXiv:2312.01336</a> <span> [<a href="https://arxiv.org/pdf/2312.01336">pdf</a>, <a href="https://arxiv.org/format/2312.01336">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"> Integrating Communication, Sensing and Computing in Satellite Internet of Things: Challenges and Opportunities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zuo%2C+Y">Yong Zuo</a>, <a href="/search/eess?searchtype=author&query=Yue%2C+M">Mingyang Yue</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Huiyuan Yang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+L">Liantao Wu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.01336v1-abstract-short" style="display: inline;"> Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing. However, the complex and dynamic satellite environments and limited network resources raise new challenges in the design of satellite IoT systems. I… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01336v1-abstract-full').style.display = 'inline'; document.getElementById('2312.01336v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.01336v1-abstract-full" style="display: none;"> Satellite Internet of Things (IoT) is to use satellites as the access points for IoT devices to achieve the global coverage of future IoT systems, and is expected to support burgeoning IoT applications, including communication, sensing, and computing. However, the complex and dynamic satellite environments and limited network resources raise new challenges in the design of satellite IoT systems. In this article, we focus on the joint design of communication, sensing, and computing to improve the performance of satellite IoT, which is quite different from the case of terrestrial IoT systems. We describe how the integration of the three functions can enhance system capabilities, and summarize the state-of-the-art solutions. Furthermore, we discuss the main challenges of integrating communication, sensing, and computing in satellite IoT to be solved with pressing interest. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01336v1-abstract-full').style.display = 'none'; document.getElementById('2312.01336v1-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 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 5 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/2311.15141">arXiv:2311.15141</a> <span> [<a href="https://arxiv.org/pdf/2311.15141">pdf</a>, <a href="https://arxiv.org/format/2311.15141">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"> OFDMA-F$^2$L: Federated Learning With Flexible Aggregation Over an OFDMA Air Interface </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+S">Shuyan Hu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wei Ni</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</a>, <a href="/search/eess?searchtype=author&query=Hossain%2C+E">Ekram Hossain</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="2311.15141v1-abstract-short" style="display: inline;"> Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-acce… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15141v1-abstract-full').style.display = 'inline'; document.getElementById('2311.15141v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.15141v1-abstract-full" style="display: none;"> Federated learning (FL) can suffer from a communication bottleneck when deployed in mobile networks, limiting participating clients and deterring FL convergence. The impact of practical air interfaces with discrete modulations on FL has not previously been studied in depth. This paper proposes a new paradigm of flexible aggregation-based FL (F$^2$L) over orthogonal frequency division multiple-access (OFDMA) air interface, termed as ``OFDMA-F$^2$L'', allowing selected clients to train local models for various numbers of iterations before uploading the models in each aggregation round. We optimize the selections of clients, subchannels and modulations, adapting to channel conditions and computing powers. Specifically, we derive an upper bound on the optimality gap of OFDMA-F$^2$L capturing the impact of the selections, and show that the upper bound is minimized by maximizing the weighted sum rate of the clients per aggregation round. A Lagrange-dual based method is developed to solve this challenging mixed integer program of weighted sum rate maximization, revealing that a ``winner-takes-all'' policy provides the almost surely optimal client, subchannel, and modulation selections. Experiments on multilayer perceptrons and convolutional neural networks show that OFDMA-F$^2$L with optimal selections can significantly improve the training convergence and accuracy, e.g., by about 18\% and 5\%, compared to potential alternatives. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.15141v1-abstract-full').style.display = 'none'; document.getElementById('2311.15141v1-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, 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">Accepted by IEEE TWC in Nov. 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14280">arXiv:2311.14280</a> <span> [<a href="https://arxiv.org/pdf/2311.14280">pdf</a>, <a href="https://arxiv.org/format/2311.14280">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"> Latent Diffusion Prior Enhanced Deep Unfolding for Snapshot Spectral Compressive Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wu%2C+Z">Zongliang Wu</a>, <a href="/search/eess?searchtype=author&query=Lu%2C+R">Ruiying Lu</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+Y">Ying Fu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2311.14280v2-abstract-short" style="display: inline;"> Snapshot compressive spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. Existing state-of-the-art methods are mostly based on deep unfolding structures but have intrinsic performance bottlenecks: $i$) the ill-posed problem of dealing with heavily degraded measurement, and $ii$) the regression loss… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14280v2-abstract-full').style.display = 'inline'; document.getElementById('2311.14280v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14280v2-abstract-full" style="display: none;"> Snapshot compressive spectral imaging reconstruction aims to reconstruct three-dimensional spatial-spectral images from a single-shot two-dimensional compressed measurement. Existing state-of-the-art methods are mostly based on deep unfolding structures but have intrinsic performance bottlenecks: $i$) the ill-posed problem of dealing with heavily degraded measurement, and $ii$) the regression loss-based reconstruction models being prone to recover images with few details. In this paper, we introduce a generative model, namely the latent diffusion model (LDM), to generate degradation-free prior to enhance the regression-based deep unfolding method. Furthermore, to overcome the large computational cost challenge in LDM, we propose a lightweight model to generate knowledge priors in deep unfolding denoiser, and integrate these priors to guide the reconstruction process for compensating high-quality spectral signal details. Numeric and visual comparisons on synthetic and real-world datasets illustrate the superiority of our proposed method in both reconstruction quality and computational efficiency. Code will be released. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14280v2-abstract-full').style.display = 'none'; document.getElementById('2311.14280v2-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 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.10803">arXiv:2311.10803</a> <span> [<a href="https://arxiv.org/pdf/2311.10803">pdf</a>, <a href="https://arxiv.org/format/2311.10803">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Robustness Enhancement in Neural Networks with Alpha-Stable Training Noise </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xueqiong Yuan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Jipeng Li</a>, <a href="/search/eess?searchtype=author&query=Kuruo%C4%9Flu%2C+E+E">Ercan Engin Kuruo臒lu</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="2311.10803v1-abstract-short" style="display: inline;"> With the increasing use of deep learning on data collected by non-perfect sensors and in non-perfect environments, the robustness of deep learning systems has become an important issue. A common approach for obtaining robustness to noise has been to train deep learning systems with data augmented with Gaussian noise. In this work, we challenge the common choice of Gaussian noise and explore the po… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10803v1-abstract-full').style.display = 'inline'; document.getElementById('2311.10803v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.10803v1-abstract-full" style="display: none;"> With the increasing use of deep learning on data collected by non-perfect sensors and in non-perfect environments, the robustness of deep learning systems has become an important issue. A common approach for obtaining robustness to noise has been to train deep learning systems with data augmented with Gaussian noise. In this work, we challenge the common choice of Gaussian noise and explore the possibility of stronger robustness for non-Gaussian impulsive noise, specifically alpha-stable noise. Justified by the Generalized Central Limit Theorem and evidenced by observations in various application areas, alpha-stable noise is widely present in nature. By comparing the testing accuracy of models trained with Gaussian noise and alpha-stable noise on data corrupted by different noise, we find that training with alpha-stable noise is more effective than Gaussian noise, especially when the dataset is corrupted by impulsive noise, thus improving the robustness of the model. The generality of this conclusion is validated through experiments conducted on various deep learning models with image and time series datasets, and other benchmark corrupted datasets. Consequently, we propose a novel data augmentation method that replaces Gaussian noise, which is typically added to the training data, with alpha-stable noise. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.10803v1-abstract-full').style.display = 'none'; document.getElementById('2311.10803v1-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 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.18630">arXiv:2310.18630</a> <span> [<a href="https://arxiv.org/pdf/2310.18630">pdf</a>, <a href="https://arxiv.org/format/2310.18630">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Joint Localization and Communication Enhancement in Uplink Integrated Sensing and Communications System with Clock Asynchronism </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xu Chen</a>, <a href="/search/eess?searchtype=author&query=He%2C+X">XinXin He</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qixun Zhang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2310.18630v1-abstract-short" style="display: inline;"> In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18630v1-abstract-full').style.display = 'inline'; document.getElementById('2310.18630v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.18630v1-abstract-full" style="display: none;"> In this paper, we propose a joint single-base localization and communication enhancement scheme for the uplink (UL) integrated sensing and communications (ISAC) system with asynchronism, which can achieve accurate single-base localization of user equipment (UE) and significantly improve the communication reliability despite the existence of timing offset (TO) due to the clock asynchronism between UE and base station (BS). Our proposed scheme integrates the CSI enhancement into the multiple signal classification (MUSIC)-based AoA estimation and thus imposes no extra complexity on the ISAC system. We further exploit a MUSIC-based range estimation method and prove that it can suppress the time-varying TO-related phase terms. Exploiting the AoA and range estimation of UE, we can estimate the location of UE. Finally, we propose a joint CSI and data signals-based localization scheme that can coherently exploit the data and the CSI signals to improve the AoA and range estimation, which further enhances the single-base localization of UE. The extensive simulation results show that the enhanced CSI can achieve equivalent bit error rate performance to the minimum mean square error (MMSE) CSI estimator. The proposed joint CSI and data signals-based localization scheme can achieve decimeter-level localization accuracy despite the existing clock asynchronism and improve the localization mean square error (MSE) by about 8 dB compared with the maximum likelihood (ML)-based benchmark method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.18630v1-abstract-full').style.display = 'none'; document.getElementById('2310.18630v1-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 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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, 11 figures, submitted to JSAC special issue "Positioning and Sensing Over Wireless Networks"</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.06382">arXiv:2310.06382</a> <span> [<a href="https://arxiv.org/pdf/2310.06382">pdf</a>, <a href="https://arxiv.org/format/2310.06382">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Mutual Information Metrics for Uplink MIMO-OFDM Integrated Sensing and Communication System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Piao%2C+J">Jinghui Piao</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+X">Xiaoyu Yang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+H">Huici Wu</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</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="2310.06382v1-abstract-short" style="display: inline;"> As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06382v1-abstract-full').style.display = 'inline'; document.getElementById('2310.06382v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.06382v1-abstract-full" style="display: none;"> As the uplink sensing has the advantage of easy implementation, it attracts great attention in integrated sensing and communication (ISAC) system. This paper presents an uplink ISAC system based on multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) technology. The mutual information (MI) is introduced as a unified metric to evaluate the performance of communication and sensing. In this paper, firstly, the upper and lower bounds of communication and sensing MI are derived in details based on the interaction between communication and sensing. And the ISAC waveform is optimized by maximizing the weighted sum of sensing and communication MI. The Monte Carlo simulation results show that, compared with other waveform optimization schemes, the proposed ISAC scheme has the best overall performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.06382v1-abstract-full').style.display = 'none'; document.getElementById('2310.06382v1-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 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/2310.05444">arXiv:2310.05444</a> <span> [<a href="https://arxiv.org/pdf/2310.05444">pdf</a>, <a href="https://arxiv.org/format/2310.05444">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"> Waveform Design for MIMO-OFDM Integrated Sensing and Communication System: An Information Theoretical Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Piao%2C+J">Jinghui Piao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+H">Huici Wu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J+A">J. Andrew Zhang</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+L">Lin Wang</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="2310.05444v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified perfor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.05444v1-abstract-full').style.display = 'inline'; document.getElementById('2310.05444v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.05444v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) is regarded as the enabling technology in the future 5th-Generation-Advanced (5G-A) and 6th-Generation (6G) mobile communication system. ISAC waveform design is critical in ISAC system. However, the difference of the performance metrics between sensing and communication brings challenges for the ISAC waveform design. This paper applies the unified performance metrics in information theory, namely mutual information (MI), to measure the communication and sensing performance in multicarrier ISAC system. In multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) ISAC system, we first derive the sensing and communication MI with subcarrier correlation and spatial correlation. Then, we propose optimal waveform designs for maximizing the sensing MI, communication MI and the weighted sum of sensing and communication MI, respectively. The optimization results are validated by Monte Carlo simulations. Our work provides effective closed-form expressions for waveform design, enabling the realization of MIMO-OFDM ISAC system with balanced performance in communication and sensing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.05444v1-abstract-full').style.display = 'none'; document.getElementById('2310.05444v1-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">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/2310.05075">arXiv:2310.05075</a> <span> [<a href="https://arxiv.org/pdf/2310.05075">pdf</a>, <a href="https://arxiv.org/format/2310.05075">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"> Decentralized Federated Learning via MIMO Over-the-Air Computation: Consensus Analysis and Performance Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhai%2C+Z">Zhiyuan Zhai</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</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="2310.05075v1-abstract-short" style="display: inline;"> Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine learning model under device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.05075v1-abstract-full').style.display = 'inline'; document.getElementById('2310.05075v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.05075v1-abstract-full" style="display: none;"> Decentralized federated learning (DFL), inherited from distributed optimization, is an emerging paradigm to leverage the explosively growing data from wireless devices in a fully distributed manner.DFL enables joint training of machine learning model under device to device (D2D) communication fashion without the coordination of a parameter server. However, the deployment of wireless DFL is facing some pivotal challenges. Communication is a critical bottleneck due to the required extensive message exchange between neighbor devices to share the learned model. Besides, consensus becomes increasingly difficult as the number of devices grows because there is no available central server to perform coordination. To overcome these difficulties, this paper proposes employing over-the-air computation (Aircomp) to improve communication efficiency by exploiting the superposition property of analog waveform in multi-access channels, and introduce the mixing matrix mechanism to promote consensus using the spectral property of symmetric doubly stochastic matrix. Specifically, we develop a novel multiple-input multiple-output over-the-air DFL (MIMO OA-DFL) framework to study over-the-air DFL problem over MIMO multiple access channels. We conduct a general convergence analysis to quantitatively capture the influence of aggregation weight and communication error on the MIMO OA-DFL performance in \emph{ad hoc} networks. The result shows that the communication error together with the spectral gap of mixing matrix has a significant impact on the learning performance. Based on this, a joint communication-learning optimization problem is formulated to optimize transceiver beamformers and mixing matrix. Extensive numerical experiments are performed to reveal the characteristics of different topologies and demonstrate the substantial learning performance enhancement of our proposed algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.05075v1-abstract-full').style.display = 'none'; document.getElementById('2310.05075v1-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 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/2310.03265">arXiv:2310.03265</a> <span> [<a href="https://arxiv.org/pdf/2310.03265">pdf</a>, <a href="https://arxiv.org/format/2310.03265">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"> Integrated Communication, Sensing, and Computation Framework for 6G Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xu Chen</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J+A">J. Andrew Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=He%2C+X">Xinxin He</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="2310.03265v1-abstract-short" style="display: inline;"> In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03265v1-abstract-full').style.display = 'inline'; document.getElementById('2310.03265v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.03265v1-abstract-full" style="display: none;"> In the sixth generation (6G) era, intelligent machine network (IMN) applications, such as intelligent transportation, require collaborative machines with communication, sensing, and computation (CSC) capabilities. This article proposes an integrated communication, sensing, and computation (ICSAC) framework for 6G to achieve the reciprocity among CSC functions to enhance the reliability and latency of communication, accuracy and timeliness of sensing information acquisition, and privacy and security of computing to realize the IMN applications. Specifically, the sensing and communication functions can merge into unified platforms using the same transmit signals, and the acquired real-time sensing information can be exploited as prior information for intelligent algorithms to enhance the performance of communication networks. This is called the computing-empowered integrated sensing and communications (ISAC) reciprocity. Such reciprocity can further improve the performance of distributed computation with the assistance of networked sensing capability, which is named the sensing-empowered integrated communications and computation (ICAC) reciprocity. The above ISAC and ICAC reciprocities can enhance each other iteratively and finally lead to the ICSAC reciprocity. To achieve these reciprocities, we explore the potential enabling technologies for the ICSAC framework. Finally, we present the evaluation results of crucial enabling technologies to show the feasibility of the ICSAC framework. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.03265v1-abstract-full').style.display = 'none'; document.getElementById('2310.03265v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 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">8 pages, 5 figures, submitted to IEEE VTM</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.02888">arXiv:2309.02888</a> <span> [<a href="https://arxiv.org/pdf/2309.02888">pdf</a>, <a href="https://arxiv.org/format/2309.02888">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"> Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cai%2C+C">Chang Cai</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y+A">Ying-Jun Angela 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="2309.02888v3-abstract-short" style="display: inline;"> In task-oriented communications, most existing work designed the physical-layer communication modules and learning based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency betwee… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02888v3-abstract-full').style.display = 'inline'; document.getElementById('2309.02888v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.02888v3-abstract-full" style="display: none;"> In task-oriented communications, most existing work designed the physical-layer communication modules and learning based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency between the design objectives may hinder the exploitation of the full benefits of task-oriented communications. In this paper, we consider a task-oriented multi-device edge inference system over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i.e., feature encoding and classification) and communication (i.e., precoding) modules are designed with the same goal of inference accuracy maximization. Instead of end-to-end learning which involves both the task dataset and wireless channel during training, we advocate a separate design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem. We cast valuable insights into this formulation and develop a block coordinate ascent (BCA) algorithm for efficient problem-solving. Moreover, the MCR2 objective serves the loss function for feature encoding and guides the classification design. Simulation results on the synthetic features explain the mechanism of MCR2 precoding at different SNRs. We also validate on the CIFAR-10 and ModelNet10 datasets that the proposed design achieves a better latency-accuracy tradeoff compared to various baselines. As such, our work paves the way for further exploration into the synergistic alignment of learning and communication objectives in task-oriented communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.02888v3-abstract-full').style.display = 'none'; document.getElementById('2309.02888v3-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 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">under minor revision in IEEE Transactions on Wireless 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/2305.17938">arXiv:2305.17938</a> <span> [<a href="https://arxiv.org/pdf/2305.17938">pdf</a>, <a href="https://arxiv.org/format/2305.17938">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"> Complex CNN CSI Enhancer for Integrated Sensing and Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xu Chen</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J+A">J. Andrew Zhang</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</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="2305.17938v2-abstract-short" style="display: inline;"> In this paper, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sensing accuracy. Within the CNN CSI enhance… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17938v2-abstract-full').style.display = 'inline'; document.getElementById('2305.17938v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.17938v2-abstract-full" style="display: none;"> In this paper, we propose a novel complex convolutional neural network (CNN) CSI enhancer for integrated sensing and communications (ISAC), which exploits the correlation between the sensing parameters (such as angle-of-arrival and range) and the channel state information (CSI) to significantly improve the CSI estimation accuracy and further enhance the sensing accuracy. Within the CNN CSI enhancer, we use the complex-valued computation layers to form the CNN, which maintains the phase information of CSI. We also transform the CSI into the sparse angle-delay domain, leading to heatmap images with prominent peaks that can be efficiently processed by CNN. Based on the enhanced CSI outputs, we further propose a novel biased fast Fourier transform (FFT)-based sensing scheme for improving the range sensing accuracy, by artificially introducing phase biasing terms. Extensive simulation results show that the ISAC complex CNN CSI enhancer can converge within 30 training epochs. The normalized mean square error (NMSE) of its CSI estimates is about 17 dB lower than that of the linear minimum mean square error (LMMSE) estimator, and the bit error rate (BER) of demodulation using the enhanced CSI estimation approaches that with perfect CSI. Finally, the range estimation MSE of the proposed biased FFT-based sensing method approaches that of the subspace-based sensing method, at a much lower complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17938v2-abstract-full').style.display = 'none'; document.getElementById('2305.17938v2-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 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">13 pages, 15 figures, submitted to IEEE Journal of Selected Topics in Signal Processing</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10299">arXiv:2305.10299</a> <span> [<a href="https://arxiv.org/pdf/2305.10299">pdf</a>, <a href="https://arxiv.org/format/2305.10299">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> <p class="title is-5 mathjax"> Binarized Spectral Compressive Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Cai%2C+Y">Yuanhao Cai</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+Y">Yuxin Zheng</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+J">Jing Lin</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yulun Zhang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Haoqian Wang</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="2305.10299v3-abstract-short" style="display: inline;"> Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI resto… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10299v3-abstract-full').style.display = 'inline'; document.getElementById('2305.10299v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10299v3-abstract-full" style="display: none;"> Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base model to be binarized. Then we present the basic unit, Binarized Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively redistribute the HSI representations before binarizing activation and uses a scalable hyperbolic tangent function to closer approximate the Sign function in backpropagation. Based on our BiSR-Conv, we customize four binarized convolutional modules to address the dimension mismatch and propagate full-precision information throughout the whole network. Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model. Comprehensive quantitative and qualitative experiments manifest that our proposed BiSRNet outperforms state-of-the-art binarization methods and achieves comparable performance with full-precision algorithms. Code and models are publicly available at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MST <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10299v3-abstract-full').style.display = 'none'; document.getElementById('2305.10299v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 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">NeurIPS 2023; The first work to study binarized spectral compressive imaging reconstruction problem</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10006">arXiv:2305.10006</a> <span> [<a href="https://arxiv.org/pdf/2305.10006">pdf</a>, <a href="https://arxiv.org/format/2305.10006">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="Computation and Language">cs.CL</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"> EfficientSCI: Densely Connected Network with Space-time Factorization for Large-scale Video Snapshot Compressive Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+L">Lishun Wang</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+M">Miao Cao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2305.10006v2-abstract-short" style="display: inline;"> Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. Although recent deep learning-based state-of-the-art (SOTA) reconstruction algorithms have achieved good results in most tasks, they still face… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10006v2-abstract-full').style.display = 'inline'; document.getElementById('2305.10006v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10006v2-abstract-full" style="display: none;"> Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. Although recent deep learning-based state-of-the-art (SOTA) reconstruction algorithms have achieved good results in most tasks, they still face the following challenges due to excessive model complexity and GPU memory limitations: 1) these models need high computational cost, and 2) they are usually unable to reconstruct large-scale video frames at high compression ratios. To address these issues, we develop an efficient network for video SCI by using dense connections and space-time factorization mechanism within a single residual block, dubbed EfficientSCI. The EfficientSCI network can well establish spatial-temporal correlation by using convolution in the spatial domain and Transformer in the temporal domain, respectively. We are the first time to show that an UHD color video with high compression ratio can be reconstructed from a snapshot 2D measurement using a single end-to-end deep learning model with PSNR above 32 dB. Extensive results on both simulation and real data show that our method significantly outperforms all previous SOTA algorithms with better real-time performance. The code is at https://github.com/ucaswangls/EfficientSCI.git. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10006v2-abstract-full').style.display = 'none'; document.getElementById('2305.10006v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.10000">arXiv:2305.10000</a> <span> [<a href="https://arxiv.org/pdf/2305.10000">pdf</a>, <a href="https://arxiv.org/format/2305.10000">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"> Over-the-Air Federated Learning in MIMO Cloud-RAN Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ma%2C+H">Haoming Ma</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+Z">Zhi Ding</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="2305.10000v1-abstract-short" style="display: inline;"> To address the limitations of traditional over-the-air federated learning (OA-FL) such as limited server coverage and low resource utilization, we propose an OA-FL in MIMO cloud radio access network (MIMO Cloud-RAN) framework, where edge devices upload (or download) model parameters to the cloud server (CS) through access points (APs). Specifically, in every training round, there are three stages:… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10000v1-abstract-full').style.display = 'inline'; document.getElementById('2305.10000v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.10000v1-abstract-full" style="display: none;"> To address the limitations of traditional over-the-air federated learning (OA-FL) such as limited server coverage and low resource utilization, we propose an OA-FL in MIMO cloud radio access network (MIMO Cloud-RAN) framework, where edge devices upload (or download) model parameters to the cloud server (CS) through access points (APs). Specifically, in every training round, there are three stages: edge aggregation; global aggregation; and model updating and broadcasting. To better utilize the correlation among APs, called inter-AP correlation, we propose modeling the global aggregation stage as a lossy distributed source coding (L-DSC) problem to make analysis from the perspective of rate-distortion theory. We further analyze the performance of the proposed OA-FL in MIMO Cloud-RAN framework. Based on the analysis, we formulate a communication-learning optimization problem to improve the system performance by considering the inter-AP correlation. To solve this problem, we develop an algorithm by using alternating optimization (AO) and majorization-minimization (MM), which effectively improves the FL learning performance. Furthermore, we propose a practical design that demonstrates the utilization of inter-AP correlation. The numerical results show that the proposed practical design effectively leverages inter-AP correlation, and outperforms other baseline schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.10000v1-abstract-full').style.display = 'none'; document.getElementById('2305.10000v1-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> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.13416">arXiv:2304.13416</a> <span> [<a href="https://arxiv.org/pdf/2304.13416">pdf</a>, <a href="https://arxiv.org/format/2304.13416">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"> DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shao%2C+S">Shitong Shao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaohan Yuan</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Z">Zhen Huang</a>, <a href="/search/eess?searchtype=author&query=Qiu%2C+Z">Ziming Qiu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuai Wang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+K">Kevin Zhou</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="2304.13416v2-abstract-short" style="display: inline;"> Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13416v2-abstract-full').style.display = 'inline'; document.getElementById('2304.13416v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.13416v2-abstract-full" style="display: none;"> Dataset expansion can effectively alleviate the problem of data scarcity for medical image segmentation, due to privacy concerns and labeling difficulties. However, existing expansion algorithms still face great challenges due to their inability of guaranteeing the diversity of synthesized images with paired segmentation masks. In recent years, Diffusion Probabilistic Models (DPMs) have shown powerful image synthesis performance, even better than Generative Adversarial Networks. Based on this insight, we propose an approach called DiffuseExpand for expanding datasets for 2D medical image segmentation using DPM, which first samples a variety of masks from Gaussian noise to ensure the diversity, and then synthesizes images to ensure the alignment of images and masks. After that, DiffuseExpand chooses high-quality samples to further enhance the effectiveness of data expansion. Our comparison and ablation experiments on COVID-19 and CGMH Pelvis datasets demonstrate the effectiveness of DiffuseExpand. Our code is released at https://github.com/shaoshitong/DiffuseExpand. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.13416v2-abstract-full').style.display = 'none'; document.getElementById('2304.13416v2-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 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">Accepted by IJCAI workshop (1st International Workshop on Generalizing from Limited Resources in the Open World). pre-version was rejected by MICCAI</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.11884">arXiv:2304.11884</a> <span> [<a href="https://arxiv.org/pdf/2304.11884">pdf</a>, <a href="https://arxiv.org/format/2304.11884">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"> Variational Bayesian Multiuser Tracking for Reconfigurable Intelligent Surface Aided MIMO-OFDM Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Teng%2C+B">Boyu Teng</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+R">Rui Wang</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="2304.11884v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing. In this paper, we study a challenging multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple RISs. In particular, we assume that a multi-a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11884v1-abstract-full').style.display = 'inline'; document.getElementById('2304.11884v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.11884v1-abstract-full" style="display: none;"> Reconfigurable intelligent surface (RIS) has attracted enormous interest for its potential advantages in assisting both wireless communication and environmental sensing. In this paper, we study a challenging multiuser tracking problem in the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system aided by multiple RISs. In particular, we assume that a multi-antenna base station (BS) receives the OFDM symbols from single-antenna users reflected by multiple RISs and tracks the positions of these users. Considering the users' mobility and the blockage of light-of-sight (LoS) paths, we establish a probability transition model to characterize the tracking process, where the geometric constraints between channel parameters and multiuser positions are utilized. We further develop an online message passing algorithm, termed the Bayesian multiuser tracking (BMT) algorithm, to estimate the multiuser positions, the angles-of-arrivals (AoAs) at multiple RISs, and the time delay and the blockage of the LoS path. The Bayesian Cramer Rao bound (BCRB) is derived as the fundamental performance limit of the considered tracking problem. Based on the BCRB, we optimize the passive beamforming (PBF) of the multiple RISs to improve the tracking performance. Simulation results show that the proposed PBF design significantly outperforms the counterpart schemes, and our BMT algorithm can achieve up to centimeter-level tracking accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.11884v1-abstract-full').style.display = 'none'; document.getElementById('2304.11884v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.09727">arXiv:2304.09727</a> <span> [<a href="https://arxiv.org/pdf/2304.09727">pdf</a>, <a href="https://arxiv.org/format/2304.09727">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"> Cooperative Multi-Cell Massive Access with Temporally Correlated Activity </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+W">Weifeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Tao%2C+M">Meixia Tao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+F">Fan Xu</a>, <a href="/search/eess?searchtype=author&query=Guan%2C+Y">Yunfeng Guan</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="2304.09727v1-abstract-short" style="display: inline;"> This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09727v1-abstract-full').style.display = 'inline'; document.getElementById('2304.09727v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.09727v1-abstract-full" style="display: none;"> This paper investigates the problem of activity detection and channel estimation in cooperative multi-cell massive access systems with temporally correlated activity, where all access points (APs) are connected to a central unit via fronthaul links. We propose to perform user-centric AP cooperation for computation burden alleviation and introduce a generalized sliding-window detection strategy for fully exploiting the temporal correlation in activity. By establishing the probabilistic model associated with the factor graph representation, we propose a scalable Dynamic Compressed Sensing-based Multiple Measurement Vector Generalized Approximate Message Passing (DCS-MMV-GAMP) algorithm from the perspective of Bayesian inference. Therein, the activity likelihood is refined by performing standard message passing among the activities in the spatial-temporal domain and GAMP is employed for efficient channel estimation. Furthermore, we develop two schemes of quantize-and-forward (QF) and detect-and-forward (DF) based on DCS-MMV-GAMP for the finite-fronthaul-capacity scenario, which are extensively evaluated under various system limits. Numerical results verify the significant superiority of the proposed approach over the benchmarks. Moreover, it is revealed that QF can usually realize superior performance when the antenna number is small, whereas DF shifts to be preferable with limited fronthaul capacity if the large-scale antenna arrays are equipped. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.09727v1-abstract-full').style.display = 'none'; document.getElementById('2304.09727v1-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 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">16 pages, 17 figures, minor revision</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.06909">arXiv:2304.06909</a> <span> [<a href="https://arxiv.org/pdf/2304.06909">pdf</a>, <a href="https://arxiv.org/format/2304.06909">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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Energy-Efficient UAV Communications in the Presence of Wind: 3D Modeling and Trajectory Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dai%2C+X">Xinhong Dai</a>, <a href="/search/eess?searchtype=author&query=Duo%2C+B">Bin Duo</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Di+Renzo%2C+M">Marco Di Renzo</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="2304.06909v2-abstract-short" style="display: inline;"> The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consumption model (GPECM) for rotary-wing UAV… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.06909v2-abstract-full').style.display = 'inline'; document.getElementById('2304.06909v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.06909v2-abstract-full" style="display: none;"> The rapid development of unmanned aerial vehicle (UAV) technology provides flexible communication services to terrestrial nodes. Energy efficiency is crucial to the deployment of UAVs, especially rotary-wing UAVs whose propulsion power is sensitive to the wind effect. In this paper, we first derive a three-dimensional (3D) generalised propulsion energy consumption model (GPECM) for rotary-wing UAVs under the consideration of stochastic wind modeling and 3D force analysis. Based on the GPECM, we study a UAV-enabled downlink communication system, where a rotary-wing UAV flies subject to stochastic wind disturbance and provides communication services for ground users (GUs). We aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing the 3D trajectory and user scheduling among the GUs based on the GPECM. We formulate the problem as stochastic optimization, which is difficult to solve due to the lack of real-time wind information. To address this issue, we propose an offline-based online adaptive (OBOA) design with two phases, namely, an offline phase and an online phase. In the offline phase, we average the wind effect on the UAV by leveraging stochastic programming (SP) based on wind statistics; then, in the online phase, we further optimize the instantaneous velocity to adapt the real-time wind. Simulation results show that the optimized trajectories of the UAV in both two phases can better adapt to the wind in changing speed and direction, and achieves a higher EE compared with the windless scheme. In particular, our proposed OBOA design can be applied in the scenario with dramatic wind changes, and makes the UAV adjust its velocity dynamically to achieve a better performance in terms of EE. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.06909v2-abstract-full').style.display = 'none'; document.getElementById('2304.06909v2-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">31 pages, 13 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/2304.04580">arXiv:2304.04580</a> <span> [<a href="https://arxiv.org/pdf/2304.04580">pdf</a>, <a href="https://arxiv.org/format/2304.04580">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"> Matrix Factorization Based Blind Bayesian Receiver for Grant-Free Random Access in mmWave MIMO mMTC </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yuan%2C+Z">Zhengdao Yuan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+F">Fei Liu</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Q">Qinghua Guo</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zhongyong Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yonghui 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="2304.04580v1-abstract-short" style="display: inline;"> Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04580v1-abstract-full').style.display = 'inline'; document.getElementById('2304.04580v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04580v1-abstract-full" style="display: none;"> Grant-free random access is promising for massive connectivity with sporadic transmissions in massive machine type communications (mMTC), where the hand-shaking between the access point (AP) and users is skipped, leading to high access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we explore the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind joint UACESD can be formulated as a constrained composite matrix factorization problem, which can be solved by exploiting the structures of the channel matrix and signal matrix. Leveraging our recently developed unitary approximate message passing based matrix factorization (UAMP-MF) algorithm, we design a message passing based Bayesian algorithm to solve the blind joint UACESD problem. Extensive simulation results demonstrate the effectiveness of the blind grant-free random access scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04580v1-abstract-full').style.display = 'none'; document.getElementById('2304.04580v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.04402">arXiv:2304.04402</a> <span> [<a href="https://arxiv.org/pdf/2304.04402">pdf</a>, <a href="https://arxiv.org/format/2304.04402">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"> Over-the-Air Federated Learning Over MIMO Channels: A Sparse-Coded Multiplexing Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chenxi Zhong</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</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="2304.04402v1-abstract-short" style="display: inline;"> The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO mult… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04402v1-abstract-full').style.display = 'inline'; document.getElementById('2304.04402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.04402v1-abstract-full" style="display: none;"> The communication bottleneck of over-the-air federated learning (OA-FL) lies in uploading the gradients of local learning models. In this paper, we study the reduction of the communication overhead in the gradients uploading by using the multiple-input multiple-output (MIMO) technique. We propose a novel sparse-coded multiplexing (SCoM) approach that employs sparse-coding compression and MIMO multiplexing to balance the communication overhead and the learning performance of the FL model. We derive an upper bound on the learning performance loss of the SCoM-based MIMO OA-FL scheme by quantitatively characterizing the gradient aggregation error. Based on the analysis results, we show that the optimal number of multiplexed data streams to minimize the upper bound on the FL learning performance loss is given by the minimum of the numbers of transmit and receive antennas. We then formulate an optimization problem for the design of precoding and post-processing matrices to minimize the gradient aggregation error. To solve this problem, we develop a low-complexity algorithm based on alternating optimization (AO) and alternating direction method of multipliers (ADMM), which effectively mitigates the impact of the gradient aggregation error. Numerical results demonstrate the superb performance of the proposed SCoM approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.04402v1-abstract-full').style.display = 'none'; document.getElementById('2304.04402v1-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.01517">arXiv:2304.01517</a> <span> [<a href="https://arxiv.org/pdf/2304.01517">pdf</a>, <a href="https://arxiv.org/ps/2304.01517">ps</a>, <a href="https://arxiv.org/format/2304.01517">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"> Code-Division OFDM Joint Communication and Sensing System for 6G Machine-type Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xu Chen</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+P">Ping Zhang</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2304.01517v1-abstract-short" style="display: inline;"> The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load-saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar sensing signals to improve the communication reliability and rada… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.01517v1-abstract-full').style.display = 'inline'; document.getElementById('2304.01517v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.01517v1-abstract-full" style="display: none;"> The joint communication and sensing (JCS) system can provide higher spectrum efficiency and load-saving for 6G machine-type communication (MTC) applications by merging necessary communication and sensing abilities with unified spectrum and transceivers. In order to suppress the mutual interference between the communication and radar sensing signals to improve the communication reliability and radar sensing accuracy, we propose a novel code-division orthogonal frequency division multiplex (CD-OFDM) JCS MTC system, where MTC users can simultaneously and continuously conduct communication and sensing with each other. {\color{black} We propose a novel CD-OFDM JCS signal and corresponding successive-interference-cancellation (SIC) based signal processing technique that obtains code-division multiplex (CDM) gain, which is compatible with the prevalent orthogonal frequency division multiplex (OFDM) communication system.} To model the unified JCS signal transmission and reception process, we propose a novel unified JCS channel model. Finally, the simulation and numerical results are shown to verify the feasibility of the CD-OFDM JCS MTC system {\color{black} and the error propagation performance}. We show that the CD-OFDM JCS MTC system can achieve not only more reliable communication but also comparably robust radar sensing compared with the precedent OFDM JCS system, especially in low signal-to-interference-and-noise ratio (SINR) regime. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.01517v1-abstract-full').style.display = 'none'; document.getElementById('2304.01517v1-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 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">13 pages,16 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12 093-12 105, Feb. 2021 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2304.01509">arXiv:2304.01509</a> <span> [<a href="https://arxiv.org/pdf/2304.01509">pdf</a>, <a href="https://arxiv.org/ps/2304.01509">ps</a>, <a href="https://arxiv.org/format/2304.01509">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 Performance of Cooperative Joint Sensing-Communication UAV Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xu Chen</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+Z">Zhiyong Feng</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+Z">Zhiqing Wei</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+F">Feifei Gao</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</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="2304.01509v1-abstract-short" style="display: inline;"> We propose a novel cooperative joint sensing-communication (JSC) unmanned aerial vehicle (UAV) network that can achieve downward-looking detection and transmit detection data simultaneously using the same time and frequency resources by exploiting the beam sharing scheme. The UAV network consists of a UAV that works as a fusion center (FCUAV) and multiple subordinate UAVs (SU). All UAVs fly at the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.01509v1-abstract-full').style.display = 'inline'; document.getElementById('2304.01509v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2304.01509v1-abstract-full" style="display: none;"> We propose a novel cooperative joint sensing-communication (JSC) unmanned aerial vehicle (UAV) network that can achieve downward-looking detection and transmit detection data simultaneously using the same time and frequency resources by exploiting the beam sharing scheme. The UAV network consists of a UAV that works as a fusion center (FCUAV) and multiple subordinate UAVs (SU). All UAVs fly at the fixed height. FCUAV integrates the sensing data of network and carries out downward-looking detection. SUs carry out downward-looking detection and transmit the sensing data to FCUAV. To achieve the beam sharing scheme, each UAV is equipped with a novel JSC antenna array that is composed of both the sensing subarray (SenA) and the communication subarray (ComA) in order to generate the sensing beam (SenB) and the communication beam (ComB) for detection and communication, respectively. SenB and ComB of each UAV share a total amount of radio power. Because of the spatial orthogonality of communication and sensing, SenB and ComB can be easily formed orthogonally. The upper bound of average cooperative sensing area (UB-ACSA) is defined as the metric to measure the sensing performance, which is related to the mutual sensing interference and the communication capacity. Numerical simulations prove the validity of the theoretical expressions for UB-ACSA of the network. The optimal number of UAVs and the optimal SenB power are identified under the total power constraint. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2304.01509v1-abstract-full').style.display = 'none'; document.getElementById('2304.01509v1-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 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">13 Pages, 14 figures, accepted by IEEE TVT</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Transactions on Vehicular Technology, vol. 69, no. 12, pp. 15545-15556, Dec. 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.09790">arXiv:2303.09790</a> <span> [<a href="https://arxiv.org/pdf/2303.09790">pdf</a>, <a href="https://arxiv.org/format/2303.09790">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"> Reliable Multimodality Eye Disease Screening via Mixture of Student's t Distributions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zou%2C+K">Ke Zou</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+T">Tian Lin</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xuedong Yuan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+H">Haoyu Chen</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xiaojing Shen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+M">Meng Wang</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+H">Huazhu Fu</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="2303.09790v4-abstract-short" style="display: inline;"> Multimodality eye disease screening is crucial in ophthalmology as it integrates information from diverse sources to complement their respective performances. However, the existing methods are weak in assessing the reliability of each unimodality, and directly fusing an unreliable modality may cause screening errors. To address this issue, we introduce a novel multimodality evidential fusion pipel… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.09790v4-abstract-full').style.display = 'inline'; document.getElementById('2303.09790v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.09790v4-abstract-full" style="display: none;"> Multimodality eye disease screening is crucial in ophthalmology as it integrates information from diverse sources to complement their respective performances. However, the existing methods are weak in assessing the reliability of each unimodality, and directly fusing an unreliable modality may cause screening errors. To address this issue, we introduce a novel multimodality evidential fusion pipeline for eye disease screening, EyeMoSt, which provides a measure of confidence for unimodality and elegantly integrates the multimodality information from a multi-distribution fusion perspective. Specifically, our model estimates both local uncertainty for unimodality and global uncertainty for the fusion modality to produce reliable classification results. More importantly, the proposed mixture of Student's $t$ distributions adaptively integrates different modalities to endow the model with heavy-tailed properties, increasing robustness and reliability. Our experimental findings on both public and in-house datasets show that our model is more reliable than current methods. Additionally, EyeMost has the potential ability to serve as a data quality discriminator, enabling reliable decision-making for multimodality eye disease screening. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.09790v4-abstract-full').style.display = 'none'; document.getElementById('2303.09790v4-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 17 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">MICCAI 2023 (Early accept):11 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/2302.12396">arXiv:2302.12396</a> <span> [<a href="https://arxiv.org/pdf/2302.12396">pdf</a>, <a href="https://arxiv.org/format/2302.12396">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"> Wireless Powered Short Packet Communications with Multiple WPT Sources </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Guo%2C+N">Ning Guo</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaopeng Yuan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Y">Yulin Hu</a>, <a href="/search/eess?searchtype=author&query=Schmeink%2C+A">Anke Schmeink</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="2302.12396v1-abstract-short" style="display: inline;"> We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by energy harvesting (EH) from multiple WPT sources, sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work for the first time characterizes the FBL reliability for such multi-source WPT enabled network and provides reliability-oriented reso… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.12396v1-abstract-full').style.display = 'inline'; document.getElementById('2302.12396v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.12396v1-abstract-full" style="display: none;"> We study a multi-source wireless power transfer (WPT) enabled network supporting multi-sensor transmissions. Activated by energy harvesting (EH) from multiple WPT sources, sensors transmit short packets to a destination with finite blocklength (FBL) codes. This work for the first time characterizes the FBL reliability for such multi-source WPT enabled network and provides reliability-oriented resource allocation designs, while a practical nonlinear EH model is considered. For scenario with a fixed frame structure, we maximize the FBL reliability via optimally allocating the transmit power among multi-source. In particular, we first investigate the relationship between the FBL reliability and multiple WPT source power, based on which a power allocation problem is formulated. To solve the formulated non-convex problem, we introduce auxiliary variables and apply successive convex approximation (SCA) technique to the non-convex component. Consequently, a sub-optimal solution can be obtained. Moreover, we extend our design into a dynamic frame structure scenario, i.e., the blocklength allocated for WPT phase and short-packet transmission phase are adjustable, which introduces more flexibility and new challenges to the system design. We provide a joint power and blocklength allocation design to minimize the system overall error probability under the total power and blocklength constraints. To address the high-dimensional optimization problem, auxiliary variables introduction, multiple variable substitutions and SCA technique utilization are exploited to reformulate and efficiently solve the problem. Finally, through numerical results, we validate our analytical model and evaluate the system performance, where a set of guidelines for practical system design are concluded. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.12396v1-abstract-full').style.display = 'none'; document.getElementById('2302.12396v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.08119">arXiv:2302.08119</a> <span> [<a href="https://arxiv.org/pdf/2302.08119">pdf</a>, <a href="https://arxiv.org/format/2302.08119">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"> A Review of Uncertainty Estimation and its Application in Medical Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zou%2C+K">Ke Zou</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhihao Chen</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xuedong Yuan</a>, <a href="/search/eess?searchtype=author&query=Shen%2C+X">Xiaojing Shen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+M">Meng Wang</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+H">Huazhu Fu</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="2302.08119v3-abstract-short" style="display: inline;"> The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the predi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.08119v3-abstract-full').style.display = 'inline'; document.getElementById('2302.08119v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.08119v3-abstract-full" style="display: none;"> The use of AI systems in healthcare for the early screening of diseases is of great clinical importance. Deep learning has shown great promise in medical imaging, but the reliability and trustworthiness of AI systems limit their deployment in real clinical scenes, where patient safety is at stake. Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician. In this paper, we review the various types of uncertainty in deep learning, including aleatoric uncertainty and epistemic uncertainty. We further discuss how they can be estimated in medical imaging. More importantly, we review recent advances in deep learning models that incorporate uncertainty estimation in medical imaging. Finally, we discuss the challenges and future directions in uncertainty estimation in deep learning for medical imaging. We hope this review will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of uncertainty estimation models in medical imaging. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.08119v3-abstract-full').style.display = 'none'; document.getElementById('2302.08119v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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">11 pages, 3 figures, 3 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.04994">arXiv:2302.04994</a> <span> [<a href="https://arxiv.org/pdf/2302.04994">pdf</a>, <a href="https://arxiv.org/format/2302.04994">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"> RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+S">Shuyan Hu</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wei Ni</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</a>, <a href="/search/eess?searchtype=author&query=Jamalipour%2C+A">Abbas Jamalipour</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="2302.04994v1-abstract-short" style="display: inline;"> This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio e… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.04994v1-abstract-full').style.display = 'inline'; document.getElementById('2302.04994v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.04994v1-abstract-full" style="display: none;"> This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance of a reconfigurable intelligent surface (RIS), we propose to augment the radio environment, suppress jamming signals, and enhance the desired signals. The UAV is designed to learn its trajectory and the RIS configuration based solely on changes in its received data rate, using the latest deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3) models. Simulations show that the proposed DRL algorithms give the UAV with strong resistance against jamming and that the TD3 algorithm exhibits faster and smoother convergence than the DDPG algorithm, and suits better for larger RISs. This DRL-based approach eliminates the need for knowledge of the channels involving the RIS and jammer, thereby offering significant practical value. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.04994v1-abstract-full').style.display = 'none'; document.getElementById('2302.04994v1-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 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 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 IoTJ in Feb. 2023</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.06223">arXiv:2301.06223</a> <span> [<a href="https://arxiv.org/pdf/2301.06223">pdf</a>, <a href="https://arxiv.org/format/2301.06223">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/TCOMM.2023.3238062">10.1109/TCOMM.2023.3238062 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xin Yuan</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+S">Shuyan Hu</a>, <a href="/search/eess?searchtype=author&query=Ni%2C+W">Wei Ni</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+R+P">Ren Ping Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+X">Xin Wang</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="2301.06223v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acqu… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.06223v1-abstract-full').style.display = 'inline'; document.getElementById('2301.06223v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.06223v1-abstract-full" style="display: none;"> Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.06223v1-abstract-full').style.display = 'none'; document.getElementById('2301.06223v1-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">accepted by IEEE TCOM in Jan. 2023</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> published under the new title "Joint User, Channel, Modulation-Coding Selection, and RIS Configuration for Jamming Resistance in Multiuser OFDMA 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