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is-link">Go</button> </div> </div> </form> </div> </div> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Zhong%2C+C&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Zhong%2C+C&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Zhong%2C+C&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> </ul> </nav> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.17759">arXiv:2409.17759</a> <span> [<a href="https://arxiv.org/pdf/2409.17759">pdf</a>, <a href="https://arxiv.org/format/2409.17759">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"> LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+Z">Zhongxin Yu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+L">Liang Chen</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+Z">Zhiyun Zeng</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+K">Kunping Yang</a>, <a href="/search/eess?searchtype=author&query=Luo%2C+S">Shaofei Luo</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+S">Shaorui Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.17759v1-abstract-short" style="display: inline;"> Capturing different intensity and directions of light rays at the same scene Light field (LF) can encode the 3D scene cues into a 4D LF image which has a wide range of applications (i.e. post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the performance of LF camera sensor. Although existing methods have achieved promising res… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17759v1-abstract-full').style.display = 'inline'; document.getElementById('2409.17759v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.17759v1-abstract-full" style="display: none;"> Capturing different intensity and directions of light rays at the same scene Light field (LF) can encode the 3D scene cues into a 4D LF image which has a wide range of applications (i.e. post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the performance of LF camera sensor. Although existing methods have achieved promising results the practical application of these models is limited because they are not lightweight enough. In this paper we propose a lightweight model named LGFN which integrates the local and global features of different views and the features of different channels for LF image SR. Specifically owing to neighboring regions of the same pixel position in different sub-aperture images exhibit similar structural relationships we design a lightweight CNN-based feature extraction module (namely DGCE) to extract local features better through feature modulation. Meanwhile as the position beyond the boundaries in the LF image presents a large disparity we propose an efficient spatial attention module (namely ESAM) which uses decomposable large-kernel convolution to obtain an enlarged receptive field and an efficient channel attention module (namely ECAM). Compared with the existing LF image SR models with large parameter our model has a parameter of 0.45M and a FLOPs of 19.33G which has achieved a competitive effect. Extensive experiments with ablation studies demonstrate the effectiveness of our proposed method which ranked the second place in the Track 2 Fidelity & Efficiency of NTIRE2024 Light Field Super Resolution Challenge and the seventh place in the Track 1 Fidelity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.17759v1-abstract-full').style.display = 'none'; document.getElementById('2409.17759v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 5 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> CVPR 2024 workshop </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.16637">arXiv:2409.16637</a> <span> [<a href="https://arxiv.org/pdf/2409.16637">pdf</a>, <a href="https://arxiv.org/ps/2409.16637">ps</a>, <a href="https://arxiv.org/format/2409.16637">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"> Deep-Learning Recognition of Scanning Transmission Electron Microscopy: Quantifying and Mitigating the Influence of Gaussian Noises </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+H">Hanlei Zhang</a>, <a href="/search/eess?searchtype=author&query=Bai%2C+J">Jincheng Bai</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiabo Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Can Li</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chuanjian Zhong</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+J">Jiye Fang</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+G">Guangwen 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="2409.16637v1-abstract-short" style="display: inline;"> Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level) and temporal (ms level) resolutions of the STEM techniques generate fruitful amounts of high-definition data, thereby enabling the high-volume and hig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16637v1-abstract-full').style.display = 'inline'; document.getElementById('2409.16637v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.16637v1-abstract-full" style="display: none;"> Scanning transmission electron microscopy (STEM) is a powerful tool to reveal the morphologies and structures of materials, thereby attracting intensive interests from the scientific and industrial communities. The outstanding spatial (atomic level) and temporal (ms level) resolutions of the STEM techniques generate fruitful amounts of high-definition data, thereby enabling the high-volume and high-speed analysis of materials. On the other hand, processing of the big dataset generated by STEM is time-consuming and beyond the capability of human-based manual work, which urgently calls for computer-based automation. In this work, we present a deep-learning mask region-based neural network (Mask R-CNN) for the recognition of nanoparticles imaged by STEM, as well as generating the associated dimensional analysis. The Mask R-CNN model was tested on simulated STEM-HAADF results with different Gaussian noises, particle shapes and particle sizes, and the results indicated that Gaussian noise has determining influence on the accuracy of recognition. By applying Gaussian and Non-Local Means filters on the noise-containing STEM-HAADF results, the influences of noises are largely mitigated, and recognition accuracy is significantly improved. This filtering-recognition approach was further applied to experimental STEM-HAADF results, which yields satisfying accuracy compared with the traditional threshold methods. The deep-learning-based method developed in this work has great potentials in analysis of the complicated structures and large data generated by STEM-HAADF. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.16637v1-abstract-full').style.display = 'none'; document.getElementById('2409.16637v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.19750">arXiv:2404.19750</a> <span> [<a href="https://arxiv.org/pdf/2404.19750">pdf</a>, <a href="https://arxiv.org/format/2404.19750">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Joint Communication and Computation Design for Distributed RISs Assisted Probabilistic Semantic Communication in IIoT </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhao%2C+Z">Zhouxiang Zhao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+L">Li Wei</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Q">Qianqian Yang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+W">Wei Xu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2404.19750v1-abstract-short" style="display: inline;"> In this paper, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in industrial Internet-of-Things (IIoT) is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19750v1-abstract-full').style.display = 'inline'; document.getElementById('2404.19750v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.19750v1-abstract-full" style="display: none;"> In this paper, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in industrial Internet-of-Things (IIoT) is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the transmission data size. To support high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following greedy policy, while the phase shift of RIS can be optimized using the tensor based beamforming. Numerical results verify the superiority of the proposed algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.19750v1-abstract-full').style.display = 'none'; document.getElementById('2404.19750v1-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> 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/2403.11974">arXiv:2403.11974</a> <span> [<a href="https://arxiv.org/pdf/2403.11974">pdf</a>, <a href="https://arxiv.org/format/2403.11974">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"> OUCopula: Bi-Channel Multi-Label Copula-Enhanced Adapter-Based CNN for Myopia Screening Based on OU-UWF Images </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+Q">Qiuyi Huang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chong Zhong</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+D">Danjuan Yang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+M">Meiyan Li</a>, <a href="/search/eess?searchtype=author&query=Welsh%2C+A+H">A. H. Welsh</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+A">Aiyi Liu</a>, <a href="/search/eess?searchtype=author&query=Fu%2C+B">Bo Fu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C+C">Catherien C. Liu</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+X">Xingtao 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="2403.11974v1-abstract-short" style="display: inline;"> Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11974v1-abstract-full').style.display = 'inline'; document.getElementById('2403.11974v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11974v1-abstract-full" style="display: none;"> Myopia screening using cutting-edge ultra-widefield (UWF) fundus imaging is potentially significant for ophthalmic outcomes. Current multidisciplinary research between ophthalmology and deep learning (DL) concentrates primarily on disease classification and diagnosis using single-eye images, largely ignoring joint modeling and prediction for Oculus Uterque (OU, both eyes). Inspired by the complex relationships between OU and the high correlation between the (continuous) outcome labels (Spherical Equivalent and Axial Length), we propose a framework of copula-enhanced adapter convolutional neural network (CNN) learning with OU UWF fundus images (OUCopula) for joint prediction of multiple clinical scores. We design a novel bi-channel multi-label CNN that can (1) take bi-channel image inputs subject to both high correlation and heterogeneity (by sharing the same backbone network and employing adapters to parameterize the channel-wise discrepancy), and (2) incorporate correlation information between continuous output labels (using a copula). Solid experiments show that OUCopula achieves satisfactory performance in myopia score prediction compared to backbone models. Moreover, OUCopula can far exceed the performance of models constructed for single-eye inputs. Importantly, our study also hints at the potential extension of the bi-channel model to a multi-channel paradigm and the generalizability of OUCopula across various backbone CNNs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11974v1-abstract-full').style.display = 'none'; document.getElementById('2403.11974v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 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/2403.11693">arXiv:2403.11693</a> <span> [<a href="https://arxiv.org/pdf/2403.11693">pdf</a>, <a href="https://arxiv.org/format/2403.11693">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"> Beamforming Design for Semantic-Bit Coexisting Communication System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Maojun Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Jin%2C+R">Richeng Jin</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+Q">Qingjiang Shi</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+K">Kaibin Huang</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="2403.11693v3-abstract-short" style="display: inline;"> Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11693v3-abstract-full').style.display = 'inline'; document.getElementById('2403.11693v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.11693v3-abstract-full" style="display: none;"> Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. Extensive simulation results demonstrate that the proposed beamforming scheme significantly outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.11693v3-abstract-full').style.display = 'none'; document.getElementById('2403.11693v3-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.01480">arXiv:2403.01480</a> <span> [<a href="https://arxiv.org/pdf/2403.01480">pdf</a>, <a href="https://arxiv.org/ps/2403.01480">ps</a>, <a href="https://arxiv.org/format/2403.01480">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"> Deep Learning-based Design of Uplink Integrated Sensing and Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qi%2C+Q">Qiao Qi</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2403.01480v1-abstract-short" style="display: inline;"> In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing tr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01480v1-abstract-full').style.display = 'inline'; document.getElementById('2403.01480v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.01480v1-abstract-full" style="display: none;"> In this paper, we investigate the issue of uplink integrated sensing and communication (ISAC) in 6G wireless networks where the sensing echo signal and the communication signal are received simultaneously at the base station (BS). To effectively mitigate the mutual interference between sensing and communication caused by the sharing of spectrum and hardware resources, we provide a joint sensing transmit waveform and communication receive beamforming design with the objective of maximizing the weighted sum of normalized sensing rate and normalized communication rate. It is formulated as a computationally complicated non-convex optimization problem, which is quite difficult to be solved by conventional optimization methods. To this end, we first make a series of equivalent transformation on the optimization problem to reduce the design complexity, and then develop a deep learning (DL)-based scheme to enhance the overall performance of ISAC. Both theoretical analysis and simulation results confirm the effectiveness and robustness of the proposed DL-based scheme for ISAC in 6G wireless networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.01480v1-abstract-full').style.display = 'none'; document.getElementById('2403.01480v1-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 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">IEEE Transactions on Wireless Communications, 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/2402.10593">arXiv:2402.10593</a> <span> [<a href="https://arxiv.org/pdf/2402.10593">pdf</a>, <a href="https://arxiv.org/format/2402.10593">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"> Bayesian Learning for Double-RIS Aided ISAC Systems with Superimposed Pilots and Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gan%2C+X">Xu Gan</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Q">Qinghua Guo</a>, <a href="/search/eess?searchtype=author&query=Yuen%2C+C">Chau Yuen</a>, <a href="/search/eess?searchtype=author&query=Debbah%2C+M">Merouane Debbah</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="2402.10593v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10593v1-abstract-full').style.display = 'inline'; document.getElementById('2402.10593v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10593v1-abstract-full" style="display: none;"> Reconfigurable intelligent surface (RIS) has great potential to improve the performance of integrated sensing and communication (ISAC) systems, especially in scenarios where line-of-sight paths between the base station and users are blocked. However, the spectral efficiency (SE) of RIS-aided ISAC uplink transmissions may be drastically reduced by the heavy burden of pilot overhead for realizing sensing capabilities. In this paper, we tackle this bottleneck by proposing a superimposed symbol scheme, which superimposes sensing pilots onto data symbols over the same time-frequency resources. Specifically, we develop a structure-aware sparse Bayesian learning framework, where decoded data symbols serve as side information to enhance sensing performance and increase SE. To meet the low-latency requirements of emerging ISAC applications, we further propose a low-complexity simultaneous communication and localization algorithm for multiple users. This algorithm employs the unitary approximate message passing in the Bayesian learning framework for initial angle estimate, followed by iterative refinements through reduced-dimension matrix calculations. Moreover, the sparse code multiple access technology is incorporated into this iterative framework for accurate data detection which also facilitates localization. Numerical results show that the proposed superimposed symbol-based scheme empowered by the developed algorithm can achieve centimeter-level localization while attaining up to $96\%$ of the SE of conventional communications without sensing capabilities. Moreover, compared to other typical ISAC schemes, the proposed superimposed symbol scheme can provide an effective throughput improvement over $133\%$. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10593v1-abstract-full').style.display = 'none'; document.getElementById('2402.10593v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.00088">arXiv:2312.00088</a> <span> [<a href="https://arxiv.org/pdf/2312.00088">pdf</a>, <a href="https://arxiv.org/ps/2312.00088">ps</a>, <a href="https://arxiv.org/format/2312.00088">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Anomaly Detection via Learning-Based Sequential Controlled Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Joseph%2C+G">Geethu Joseph</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">M. Cenk Gursoy</a>, <a href="/search/eess?searchtype=author&query=Velipasalar%2C+S">Senem Velipasalar</a>, <a href="/search/eess?searchtype=author&query=Varshney%2C+P+K">Pramod K. Varshney</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="2312.00088v1-abstract-short" style="display: inline;"> In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the process is anomalous. To identify the anomalies, the decision-making agent is allowed to observe a subset of the processes at each time instant. Also, probing each process has an as… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.00088v1-abstract-full').style.display = 'inline'; document.getElementById('2312.00088v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.00088v1-abstract-full" style="display: none;"> In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the process is anomalous. To identify the anomalies, the decision-making agent is allowed to observe a subset of the processes at each time instant. Also, probing each process has an associated cost. Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost. We cast this problem as a sequential hypothesis testing problem within the framework of Markov decision processes. This formulation utilizes both a Bayesian log-likelihood ratio-based reward and an entropy-based reward. The problem is then solved using two approaches: 1) a deep reinforcement learning-based approach where we design both deep Q-learning and policy gradient actor-critic algorithms; and 2) a deep active inference-based approach. Using numerical experiments, we demonstrate the efficacy of our algorithms and show that our algorithms adapt to any unknown statistical dependence pattern of the processes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.00088v1-abstract-full').style.display = 'none'; document.getElementById('2312.00088v1-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 November, 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/2310.04972">arXiv:2310.04972</a> <span> [<a href="https://arxiv.org/pdf/2310.04972">pdf</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 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/10.19912/j.0254-0096.tynxb.2022-1463">10.19912/j.0254-0096.tynxb.2022-1463 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Secondary frequency control of islanded microgrid considering wind and solar stochastics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+Z">Zhifu Jiang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xiangyu Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jikai Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang 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="2310.04972v2-abstract-short" style="display: inline;"> This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variab… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04972v2-abstract-full').style.display = 'inline'; document.getElementById('2310.04972v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.04972v2-abstract-full" style="display: none;"> This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reduce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.04972v2-abstract-full').style.display = 'none'; document.getElementById('2310.04972v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 7 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">Accepted by Acta energiae solaris sinica [In Chinese]</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Acta Energiae Solaris Sinica 45 (2024) 523-533 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2308.13211">arXiv:2308.13211</a> <span> [<a href="https://arxiv.org/pdf/2308.13211">pdf</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 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.epsr.2023.109793">10.1016/j.epsr.2023.109793 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Model predictive control strategy in waked wind farms for optimal fatigue loads </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+Y">Yicheng Ding</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+H">Husai Wang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jikai Chen</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Jian Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang 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="2308.13211v1-abstract-short" style="display: inline;"> With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13211v1-abstract-full').style.display = 'inline'; document.getElementById('2308.13211v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2308.13211v1-abstract-full" style="display: none;"> With the rapid growth of wind power penetration, wind farms (WFs) are required to implement frequency regulation that active power control to track a given power reference. Due to the wake interaction of the wind turbines (WTs), there is more than one solution to distributing power reference among the operating WTs, which can be exploited as an optimization problem for the second goal, such as fatigue load alleviation. In this paper, a closed-loop model predictive controller is developed that minimizes the wind farm tracking errors, the dynamical fatigue load, and and the load equalization. The controller is evaluated in a mediumfidelity model. A 64 WTs simulation case study is used to demonstrate the control performance for different penalty factor settings. The results indicated the WF can alleviate dynamical fatigue load and have no significant impact on power tracking. However, the uneven load distribution in the wind turbine system poses challenges for maintenance. By adding a trade-off between the load equalization and dynamical fatigue load, the load differences between WTs are significantly reduced, while the dynamical fatigue load slightly increases when selecting a proper penalty factor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2308.13211v1-abstract-full').style.display = 'none'; document.getElementById('2308.13211v1-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 August, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by Electric Power Systems Research</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Electric Power Systems Research 224 (2023) 109793 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.04915">arXiv:2306.04915</a> <span> [<a href="https://arxiv.org/pdf/2306.04915">pdf</a>, <a href="https://arxiv.org/ps/2306.04915">ps</a>, <a href="https://arxiv.org/format/2306.04915">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-based Beamforming Design for Joint Performance Enhancement of RIS-Aided ISAC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qian%2C+X">Xiaowei Qian</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chenxi Liu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+M">Mugen Peng</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2306.04915v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RI… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04915v1-abstract-full').style.display = 'inline'; document.getElementById('2306.04915v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.04915v1-abstract-full" style="display: none;"> Reconfigurable intelligent surface (RIS) has shown its great potential in facilitating device-based integrated sensing and communication (ISAC), where sensing and communication tasks are mostly conducted on different time-frequency resources. While the more challenging scenarios of simultaneous sensing and communication (SSC) have so far drawn little attention. In this paper, we propose a novel RIS-aided ISAC framework where the inherent location information in the received communication signals from a blind-zone user equipment is exploited to enable SSC. We first design a two-phase ISAC transmission protocol. In the first phase, communication and coarse-grained location sensing are performed concurrently by exploiting the very limited channel state information, while in the second phase, by using the coarse-grained sensing information obtained from the first phase, simple-yet-efficient sensing-based beamforming designs are proposed to realize both higher-rate communication and fine-grained location sensing. We demonstrate that our proposed framework can achieve almost the same performance as the communication-only frameworks, while providing up to millimeter-level positioning accuracy. In addition, we show how the communication and sensing performance can be simultaneously boosted through our proposed sensing-based beamforming designs. The results presented in this work provide valuable insights into the design and implementation of other ISAC systems considering SSC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.04915v1-abstract-full').style.display = 'none'; document.getElementById('2306.04915v1-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 June, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/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/2303.11319">arXiv:2303.11319</a> <span> [<a href="https://arxiv.org/pdf/2303.11319">pdf</a>, <a href="https://arxiv.org/format/2303.11319">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 Edge Learning with Error-Feedback One-Bit Quantization and Power Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yuding Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+D">Dongzhu Liu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+Q">Qingjiang Shi</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2303.11319v1-abstract-short" style="display: inline;"> Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. A one-bit digital over-the-air aggregation (OBDA) scheme has been recently… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11319v1-abstract-full').style.display = 'inline'; document.getElementById('2303.11319v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.11319v1-abstract-full" style="display: none;"> Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framework enables all edge devices to transmit model updates simultaneously over the entire available bandwidth, allowing for over-the-air aggregation. A one-bit digital over-the-air aggregation (OBDA) scheme has been recently proposed, featuring one-bit gradient quantization at edge devices and majority-voting based decoding at the edge server. However, the low-resolution one-bit gradient quantization slows down the model convergence and leads to performance degradation. On the other hand, the aggregation errors caused by fading channels in Air-FEEL is still remained to be solved. To address these issues, we propose the error-feedback one-bit broadband digital aggregation (EFOBDA) and an optimized power control policy. To this end, we first provide a theoretical analysis to evaluate the impact of error feedback on the convergence of FL with EFOBDA. The analytical results show that, by setting an appropriate feedback strength, EFOBDA is comparable to the Air-FEEL without quantization, thus enhancing the performance of OBDA. Then, we further introduce a power control policy by maximizing the convergence rate under instantaneous power constraints. The convergence analysis and optimized power control policy are verified by the experiments, which show that the proposed scheme achieves significantly faster convergence and higher test accuracy in image classification tasks compared with the one-bit quantization scheme without error feedback or optimized power control policy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.11319v1-abstract-full').style.display = 'none'; document.getElementById('2303.11319v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.00461">arXiv:2302.00461</a> <span> [<a href="https://arxiv.org/pdf/2302.00461">pdf</a>, <a href="https://arxiv.org/ps/2302.00461">ps</a>, <a href="https://arxiv.org/format/2302.00461">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"> AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.00461v1-abstract-short" style="display: inline;"> In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which compresses the received pilot signal and makes channel estimation a compressive sensing (CS) problem. However, existing high-performance CS algorithms usually suffer from high complexity. On the other hand, the beam squin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.00461v1-abstract-full').style.display = 'inline'; document.getElementById('2302.00461v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.00461v1-abstract-full" style="display: none;"> In wideband millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, channel estimation is challenging due to the hybrid analog-digital architecture, which compresses the received pilot signal and makes channel estimation a compressive sensing (CS) problem. However, existing high-performance CS algorithms usually suffer from high complexity. On the other hand, the beam squint effect caused by huge bandwidth and massive antennas will deteriorate estimation performance. In this paper, frequency-dependent angular dictionaries are first adopted to compensate for beam squint. Then, the expectation-maximization (EM)-based sparse Bayesian learning (SBL) algorithm is enhanced in two aspects, where the E-step in each iteration is implemented by approximate message passing (AMP) to reduce complexity while the M-step is realized by a deep neural network (DNN) to improve performance. In simulation, the proposed AMP-SBL unfolding-based channel estimator achieves satisfactory performance with low complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.00461v1-abstract-full').style.display = 'none'; document.getElementById('2302.00461v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 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/2212.00227">arXiv:2212.00227</a> <span> [<a href="https://arxiv.org/pdf/2212.00227">pdf</a>, <a href="https://arxiv.org/format/2212.00227">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"> Wireless Image Transmission with Semantic and Security Awareness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Maojun Zhang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zezhong Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2212.00227v1-abstract-short" style="display: inline;"> Semantic communication is an increasingly popular framework for wireless image transmission due to its high communication efficiency. With the aid of the joint-source-and-channel (JSC) encoder implemented by neural network, semantic communication directly maps original images into symbol sequences containing semantic information. Compared with the traditional separate source and channel coding des… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00227v1-abstract-full').style.display = 'inline'; document.getElementById('2212.00227v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2212.00227v1-abstract-full" style="display: none;"> Semantic communication is an increasingly popular framework for wireless image transmission due to its high communication efficiency. With the aid of the joint-source-and-channel (JSC) encoder implemented by neural network, semantic communication directly maps original images into symbol sequences containing semantic information. Compared with the traditional separate source and channel coding design used in bitlevel communication systems, semantic communication systems are known to be more efficient and accurate especially in the low signal-to-the-noise ratio (SNR) regime. This thus prompts an critical while yet to be tackled issue of security in semantic communication: it makes the eavesdropper more easier to crack the semantic information as it can be decoded even in a quite noisy channel. In this letter, we develop a semantic communication framework that accounts for both semantic meaning decoding efficiency and its risk of privacy leakage. To achieve this, targeting wireless image transmission, we on the one hand propose an JSC autoencoder featuring residual for efficient semantic meaning extraction and transmission, and on the other hand, propose a data-driven scheme that balances the efficiency-privacy tradeoff. Extensive experimental results are provided to show the effectiveness and robustness of the proposed scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2212.00227v1-abstract-full').style.display = 'none'; document.getElementById('2212.00227v1-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 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Submitted to IEEE WCL for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2209.11624">arXiv:2209.11624</a> <span> [<a href="https://arxiv.org/pdf/2209.11624">pdf</a>, <a href="https://arxiv.org/ps/2209.11624">ps</a>, <a href="https://arxiv.org/format/2209.11624">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-Assisted Hierarchical Aggregation for Over-the-Air Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+X">Xiangyu Zhong</a>, <a href="/search/eess?searchtype=author&query=Yuan%2C+X">Xiaojun Yuan</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Huiyuan Yang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chenxi Zhong</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="2209.11624v1-abstract-short" style="display: inline;"> With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issues will significantly reduce the learning performance. In this paper, we propose… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11624v1-abstract-full').style.display = 'inline'; document.getElementById('2209.11624v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2209.11624v1-abstract-full" style="display: none;"> With huge amounts of data explosively increasing in the mobile edge, over-the-air federated learning (OA-FL) emerges as a promising technique to reduce communication costs and privacy leak risks. However, when devices in a relatively large area cooperatively train a machine learning model, the attendant straggler issues will significantly reduce the learning performance. In this paper, we propose an unmanned aerial vehicle (UAV) assisted OA-FL system, where the UAV acts as a parameter server (PS) to aggregate the local gradients hierarchically for global model updating. Under this UAV-assisted hierarchical aggregation scheme, we carry out a gradient-correlation-aware FL performance analysis. We then formulate a mean squared error (MSE) minimization problem to tune the UAV trajectory and the global aggregation coefficients based on the analysis results. An algorithm based on alternating optimization (AO) and successive convex approximation (SCA) is developed to solve the formulated problem. Simulation results demonstrate the great potential of our UAV-assisted hierarchical aggregation scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2209.11624v1-abstract-full').style.display = 'none'; document.getElementById('2209.11624v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 September, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.06072">arXiv:2208.06072</a> <span> [<a href="https://arxiv.org/pdf/2208.06072">pdf</a>, <a href="https://arxiv.org/ps/2208.06072">ps</a>, <a href="https://arxiv.org/format/2208.06072">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"> Multiple RISs Assisted Cell-Free Networks With Two-timescale CSI: Performance Analysis and System Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gan%2C+X">Xu Gan</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2208.06072v1-abstract-short" style="display: inline;"> Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06072v1-abstract-full').style.display = 'inline'; document.getElementById('2208.06072v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.06072v1-abstract-full" style="display: none;"> Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of $\mathcal{O}(M)$ is achieved, with $M$ being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.06072v1-abstract-full').style.display = 'none'; document.getElementById('2208.06072v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">31 pages, 9 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.05479">arXiv:2208.05479</a> <span> [<a href="https://arxiv.org/pdf/2208.05479">pdf</a>, <a href="https://arxiv.org/format/2208.05479">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"> IRS-Based Integrated Location Sensing and Communication for mmWave SIMO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chenxi Liu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+M">Mugen Peng</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2208.05479v1-abstract-short" style="display: inline;"> In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be carried out simultaneously, sharing the same frequency and time resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission prot… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05479v1-abstract-full').style.display = 'inline'; document.getElementById('2208.05479v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.05479v1-abstract-full" style="display: none;"> In this paper, we establish an integrated sensing and communication (ISAC) system based on a distributed semi-passive intelligent reflecting surface (IRS), which allows location sensing and data transmission to be carried out simultaneously, sharing the same frequency and time resources. The detailed working process of the proposed IRS-based ISAC system is designed, including the transmission protocol, location sensing and beamforming optimization. Specifically, each coherence block consists of two periods, the ISAC period with two time blocks and the pure communication (PC) period. During each time block of the ISAC period, data transmission and user positioning are carried out simultaneously. The estimated user location in the first time block will be used for beamforming design in the second time block. During the PC period, only data transmission is conducted, by invoking the user location estimated in the second time block of the ISAC period for beamforming design. {\color{black}Simulation results show that a millimeter-level positioning accuracy can be achieved by the proposed location sensing scheme, demonstrating the advantage of the proposed IRS-based ISAC framework. Besides, the proposed two beamforming schemes based on the estimated location information achieve similar performance to the benchmark schemes assuming perfect channel state information (CSI), which verifies the effectiveness of beamforming design using sensed location information. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05479v1-abstract-full').style.display = 'none'; document.getElementById('2208.05479v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: text overlap with arXiv:2208.05300</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.05324">arXiv:2208.05324</a> <span> [<a href="https://arxiv.org/pdf/2208.05324">pdf</a>, <a href="https://arxiv.org/ps/2208.05324">ps</a>, <a href="https://arxiv.org/format/2208.05324">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"> IRS-Aided Non-Orthogonal ISAC Systems: Performance Analysis and Beamforming Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+Z">Zhouyuan Yu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chenxi Liu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+M">Mugen Peng</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2208.05324v1-abstract-short" style="display: inline;"> Intelligent reflecting surface (IRS) has shown its effectiveness in facilitating orthogonal time-division integrated sensing and communications (TD-ISAC), in which the sensing task and the communication task occupy orthogonal time-frequency resources, while the role of IRS in the more interesting scenarios of non-orthogonal ISAC (NO-ISAC) systems has so far remained unclear. In this paper, we cons… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05324v1-abstract-full').style.display = 'inline'; document.getElementById('2208.05324v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.05324v1-abstract-full" style="display: none;"> Intelligent reflecting surface (IRS) has shown its effectiveness in facilitating orthogonal time-division integrated sensing and communications (TD-ISAC), in which the sensing task and the communication task occupy orthogonal time-frequency resources, while the role of IRS in the more interesting scenarios of non-orthogonal ISAC (NO-ISAC) systems has so far remained unclear. In this paper, we consider an IRS-aided NO-ISAC system, where a distributed IRS is deployed to assist concurrent communication and location sensing for a blind-zone user, occupying non-orthogonal/overlapped time-frequency resources. We first propose a modified Cramer-Rao lower bound (CRLB) to characterize the performances of both communication and location sensing in a unified manner. We further derive the closed-form expressions of the modified CRLB in our considered NO-ISAC system, enabling us to identify the fundamental trade-off between the communication and location sensing performances. In addition, by exploiting the modified CRLB, we propose a joint active and passive beamforming design algorithm that achieves a good communication and location sensing trade-off. Through numerical results, we demonstrate the superiority of the IRS-aided NO-ISAC systems over the IRS-aided TD-ISAC systems, in terms of both communication and localization performances. Besides, it is shown that the IRS-aided NO-ISAC system with random communication signals can achieve comparable localization performance to the IRS-aided localization system with dedicated positioning reference signals. Moreover, we investigate the trade-off between communication performance and localization performance and show how the performance of the NO-ISAC system can be significantly boosted by increasing the number of the IRS elements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05324v1-abstract-full').style.display = 'none'; document.getElementById('2208.05324v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2208.05300">arXiv:2208.05300</a> <span> [<a href="https://arxiv.org/pdf/2208.05300">pdf</a>, <a href="https://arxiv.org/ps/2208.05300">ps</a>, <a href="https://arxiv.org/format/2208.05300">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/TSP.2022.3217353">10.1109/TSP.2022.3217353 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Location Sensing and Beamforming Design for IRS-Enabled Multi-User ISAC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yu%2C+Z">Zhouyuan Yu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+C">Chenxi Liu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+M">Mugen Peng</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2208.05300v1-abstract-short" style="display: inline;"> This paper explores the potential of the intelligent reflecting surface (IRS) in realizing multi-user concurrent communication and localization, using the same time-frequency resources. Specifically, we propose an IRS-enabled multi-user integrated sensing and communication (ISAC) framework, where a distributed semi-passive IRS assists the uplink data transmission from multiple users to the base st… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05300v1-abstract-full').style.display = 'inline'; document.getElementById('2208.05300v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.05300v1-abstract-full" style="display: none;"> This paper explores the potential of the intelligent reflecting surface (IRS) in realizing multi-user concurrent communication and localization, using the same time-frequency resources. Specifically, we propose an IRS-enabled multi-user integrated sensing and communication (ISAC) framework, where a distributed semi-passive IRS assists the uplink data transmission from multiple users to the base station (BS) and conducts multi-user localization, simultaneously. We first design an ISAC transmission protocol, where the whole transmission period consists of two periods, i.e., the ISAC period for simultaneous uplink communication and multi-user localization, and the pure communication (PC) period for only uplink data transmission. For the ISAC period, we propose a multi-user location sensing algorithm, which utilizes the uplink communication signals unknown to the IRS, thus removing the requirement of dedicated positioning reference signals in conventional location sensing methods. Based on the sensed users' locations, we propose two novel beamforming algorithms for the ISAC period and PC period, respectively, which can work with discrete phase shifts and require no channel state information (CSI) acquisition. Numerical results show that the proposed multi-user location sensing algorithm can achieve up to millimeter-level positioning accuracy, indicating the advantage of the IRS-enabled ISAC framework. Moreover, the proposed beamforming algorithms with sensed location information and discrete phase shifts can achieve comparable performance to the benchmark considering perfect CSI acquisition and continuous phase shifts, demonstrating how the location information can ensure the communication performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.05300v1-abstract-full').style.display = 'none'; document.getElementById('2208.05300v1-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.03765">arXiv:2207.03765</a> <span> [<a href="https://arxiv.org/pdf/2207.03765">pdf</a>, <a href="https://arxiv.org/ps/2207.03765">ps</a>, <a href="https://arxiv.org/format/2207.03765">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Deep Learning-Based Framework for Low Complexity Multi-User MIMO Precoding Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Maojun Zhang</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2207.03765v1-abstract-short" style="display: inline;"> Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03765v1-abstract-full').style.display = 'inline'; document.getElementById('2207.03765v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.03765v1-abstract-full" style="display: none;"> Using precoding to suppress multi-user interference is a well-known technique to improve spectra efficiency in multiuser multiple-input multiple-output (MU-MIMO) systems, and the pursuit of high performance and low complexity precoding method has been the focus in the last decade. The traditional algorithms including the zero-forcing (ZF) algorithm and the weighted minimum mean square error (WMMSE) algorithm failed to achieve a satisfactory trade-off between complexity and performance. In this paper, leveraging on the power of deep learning, we propose a low-complexity precoding design framework for MU-MIMO systems. The key idea is to transform the MIMO precoding problem into the multiple-input single-output precoding problem, where the optimal precoding structure can be obtained in closed-form. A customized deep neural network is designed to fit the mapping from the channels to the precoding matrix. In addition, the technique of input dimensionality reduction, network pruning, and recovery module compression are used to further improve the computational efficiency. Furthermore, the extension to the practical MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) system is studied. Simulation results show that the proposed low-complexity precoding scheme achieves similar performance as the WMMSE algorithm with very low computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03765v1-abstract-full').style.display = 'none'; document.getElementById('2207.03765v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2207.03634">arXiv:2207.03634</a> <span> [<a href="https://arxiv.org/pdf/2207.03634">pdf</a>, <a href="https://arxiv.org/ps/2207.03634">ps</a>, <a href="https://arxiv.org/format/2207.03634">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Integrating Sensing, Computing, and Communication in 6G Wireless Networks: Design and Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qi%2C+Q">Qiao Qi</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Khalili%2C+A">Ata Khalili</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</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="2207.03634v1-abstract-short" style="display: inline;"> The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03634v1-abstract-full').style.display = 'inline'; document.getElementById('2207.03634v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2207.03634v1-abstract-full" style="display: none;"> The roll-out of various emerging wireless services has triggered the need for the sixth-generation (6G) wireless networks to provide functions of target sensing, intelligent computing and information communication over the same radio spectrum. In this paper, we provide a unified framework integrating sensing, computing, and communication to optimize limited system resource for 6G wireless networks. In particular, two typical joint beamforming design algorithms are derived based on multi-objective optimization problems (MOOP) with the goals of the weighted overall performance maximization and the total transmit power minimization, respectively. Extensive simulation results validate the effectiveness of the proposed algorithms. Moreover, the impacts of key system parameters are revealed to provide useful insights for the design of integrated sensing, computing, and communication (ISCC). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2207.03634v1-abstract-full').style.display = 'none'; document.getElementById('2207.03634v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 July, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2022. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">IEEE Transactions on Communications, 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2205.05202">arXiv:2205.05202</a> <span> [<a href="https://arxiv.org/pdf/2205.05202">pdf</a>, <a href="https://arxiv.org/ps/2205.05202">ps</a>, <a href="https://arxiv.org/format/2205.05202">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"> Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a>, <a href="/search/eess?searchtype=author&query=Soriaga%2C+J+B">Joseph B. Soriaga</a>, <a href="/search/eess?searchtype=author&query=Behboodi%2C+A">Arash Behboodi</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="2205.05202v1-abstract-short" style="display: inline;"> Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been develope… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.05202v1-abstract-full').style.display = 'inline'; document.getElementById('2205.05202v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2205.05202v1-abstract-full" style="display: none;"> Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD is challenging due to only limited radio frequency (RF) chains at transceivers. Although various compressive sensing (CS) algorithms have been developed to solve this problem by exploiting inherent channel sparsity and sparsity structures, practical effects, such as power leakage and beam squint, can still make the real channel features deviate from the assumed models and result in performance degradation. Also, the high complexity of CS algorithms caused by a large number of iterations hinders their applications in practice. To tackle these issues, we develop a deep learning (DL)-based channel estimation approach where the sparse Bayesian learning (SBL) algorithm is unfolded into a deep neural network (DNN). In each SBL layer, Gaussian variance parameters of the sparse angular domain channel are updated by a tailored DNN, which is able to effectively capture complicated channel sparsity structures in various domains. Besides, the measurement matrix is jointly optimized for performance improvement. Then, the proposed approach is extended to the multi-block case where channel correlation in time is further exploited to adaptively predict the measurement matrix and facilitate the update of Gaussian variance parameters. Based on simulation results, the proposed approaches significantly outperform existing approaches but with reduced complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2205.05202v1-abstract-full').style.display = 'none'; document.getElementById('2205.05202v1-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.03244">arXiv:2202.03244</a> <span> [<a href="https://arxiv.org/pdf/2202.03244">pdf</a>, <a href="https://arxiv.org/ps/2202.03244">ps</a>, <a href="https://arxiv.org/format/2202.03244">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"> Online Deep Neural Network for Optimization in Wireless Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2202.03244v1-abstract-short" style="display: inline;"> Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. However, most current offline DNN-based methods still suffer from unsatisfactory performance, limited generalization ability, and poor interpretability. In this article, we propose an online DNN-based approach to solve gene… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03244v1-abstract-full').style.display = 'inline'; document.getElementById('2202.03244v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.03244v1-abstract-full" style="display: none;"> Recently, deep neural network (DNN) has been widely adopted in the design of intelligent communication systems thanks to its strong learning ability and low testing complexity. However, most current offline DNN-based methods still suffer from unsatisfactory performance, limited generalization ability, and poor interpretability. In this article, we propose an online DNN-based approach to solve general optimization problems in wireless communications, where a dedicated DNN is trained for each data sample. By treating the optimization variables and the objective function as network parameters and loss function, respectively, the optimization problem can be solved equivalently through network training. Thanks to the online optimization nature and meaningful network parameters, the proposed approach owns strong generalization ability and interpretability, while its superior performance is demonstrated through a practical example of joint beamforming in intelligent reflecting surface (IRS)-aided multi-user multiple-input multiple-output (MIMO) systems. Simulation results show that the proposed online DNN outperforms conventional offline DNN and state-of-the-art iterative optimization algorithm, but with low complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03244v1-abstract-full').style.display = 'none'; document.getElementById('2202.03244v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2202.03220">arXiv:2202.03220</a> <span> [<a href="https://arxiv.org/pdf/2202.03220">pdf</a>, <a href="https://arxiv.org/ps/2202.03220">ps</a>, <a href="https://arxiv.org/format/2202.03220">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/TWC.2021.3137354">10.1109/TWC.2021.3137354 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2202.03220v1-abstract-short" style="display: inline;"> Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03220v1-abstract-full').style.display = 'inline'; document.getElementById('2202.03220v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2202.03220v1-abstract-full" style="display: none;"> Accurate and efficient estimation of the high dimensional channels is one of the critical challenges for practical applications of massive multiple-input multiple-output (MIMO). In the context of hybrid analog-digital (HAD) transceivers, channel estimation becomes even more complicated due to information loss caused by limited radio-frequency chains. The conventional compressive sensing (CS) algorithms usually suffer from unsatisfactory performance and high computational complexity. In this paper, we propose a novel deep learning (DL) based framework for uplink channel estimation in HAD massive MIMO systems. To better exploit the sparsity structure of channels in the angular domain, a novel angular space segmentation method is proposed, where the entire angular space is segmented into many small regions and a dedicated neural network is trained offline for each region. During online testing, the most suitable network is selected based on the information from the global positioning system. Inside each neural network, the region-specific measurement matrix and channel estimator are jointly optimized, which not only improves the signal measurement efficiency, but also enhances the channel estimation capability. Simulation results show that the proposed approach significantly outperforms the state-of-the-art CS algorithms in terms of estimation performance and computational complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2202.03220v1-abstract-full').style.display = 'none'; document.getElementById('2202.03220v1-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 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.14709">arXiv:2112.14709</a> <span> [<a href="https://arxiv.org/pdf/2112.14709">pdf</a>, <a href="https://arxiv.org/ps/2112.14709">ps</a>, <a href="https://arxiv.org/format/2112.14709">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"> Dynamic Channel Access and Power Control in Wireless Interference Networks via Multi-Agent Deep Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lu%2C+Z">Ziyang Lu</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">M. Cenk Gursoy</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="2112.14709v1-abstract-short" style="display: inline;"> Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-age… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14709v1-abstract-full').style.display = 'inline'; document.getElementById('2112.14709v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.14709v1-abstract-full" style="display: none;"> Due to the scarcity in the wireless spectrum and limited energy resources especially in mobile applications, efficient resource allocation strategies are critical in wireless networks. Motivated by the recent advances in deep reinforcement learning (DRL), we address multi-agent DRL-based joint dynamic channel access and power control in a wireless interference network. We first propose a multi-agent DRL algorithm with centralized training (DRL-CT) to tackle the joint resource allocation problem. In this case, the training is performed at the central unit (CU) and after training, the users make autonomous decisions on their transmission strategies with only local information. We demonstrate that with limited information exchange and faster convergence, DRL-CT algorithm can achieve 90% of the performance achieved by the combination of weighted minimum mean square error (WMMSE) algorithm for power control and exhaustive search for dynamic channel access. In the second part of this paper, we consider distributed multi-agent DRL scenario in which each user conducts its own training and makes its decisions individually, acting as a DRL agent. Finally, as a compromise between centralized and fully distributed scenarios, we consider federated DRL (FDRL) to approach the performance of DRL-CT with the use of a central unit in training while limiting the information exchange and preserving privacy of the users in the wireless system. Via simulation results, we show that proposed learning frameworks lead to efficient adaptive channel access and power control policies in dynamic environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14709v1-abstract-full').style.display = 'none'; document.getElementById('2112.14709v1-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.14024">arXiv:2112.14024</a> <span> [<a href="https://arxiv.org/pdf/2112.14024">pdf</a>, <a href="https://arxiv.org/ps/2112.14024">ps</a>, <a href="https://arxiv.org/format/2112.14024">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"> Unsourced Random Massive Access with Beam-Space Tree Decoding </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Che%2C+J">Jingze Che</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Z">Zhaohui Yang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Ng%2C+D+W+K">Derrick Wing Kwan Ng</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="2112.14024v2-abstract-short" style="display: inline;"> The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input mul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14024v2-abstract-full').style.display = 'inline'; document.getElementById('2112.14024v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.14024v2-abstract-full" style="display: none;"> The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.14024v2-abstract-full').style.display = 'none'; document.getElementById('2112.14024v2-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by IEEE JSAC special issue on Next Generation Multiple Access</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.13603">arXiv:2112.13603</a> <span> [<a href="https://arxiv.org/pdf/2112.13603">pdf</a>, <a href="https://arxiv.org/format/2112.13603">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="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Over-the-Air Federated Multi-Task Learning Over MIMO Multiple Access Channels </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=Yang%2C+H">Huiyuan Yang</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="2112.13603v2-abstract-short" style="display: inline;"> With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superpositio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.13603v2-abstract-full').style.display = 'inline'; document.getElementById('2112.13603v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.13603v2-abstract-full" style="display: none;"> With the explosive growth of data and wireless devices, federated learning (FL) over wireless medium has emerged as a promising technology for large-scale distributed intelligent systems. Yet, the urgent demand for ubiquitous intelligence will generate a large number of concurrent FL tasks, which may seriously aggravate the scarcity of communication resources. By exploiting the analog superposition of electromagnetic waves, over-the-air computation (AirComp) is an appealing solution to alleviate the burden of communication required by FL. However, sharing frequency-time resources in over-the-air computation inevitably brings about the problem of inter-task interference, which poses a new challenge that needs to be appropriately addressed. In this paper, we study over-the-air federated multi-task learning (OA-FMTL) over the multiple-input multiple-output (MIMO) multiple access (MAC) channel. We propose a novel model aggregation method for the alignment of local gradients of different devices, which alleviates the straggler problem in over-the-air computation due to the channel heterogeneity. We establish a communication-learning analysis framework for the proposed OA-FMTL scheme by considering the spatial correlation between devices, and formulate an optimization problem for the design of transceiver beamforming and device selection. To solve this problem, we develop an algorithm by using alternating optimization (AO) and fractional programming (FP), which effectively mitigates the impact of inter-task interference on the FL learning performance. We show that due to the use of the new model aggregation method, device selection is no longer essential, thereby avoiding the heavy computational burden involved in selecting active devices. Numerical results demonstrate the validity of the analysis and the superb performance of the proposed scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.13603v2-abstract-full').style.display = 'none'; document.getElementById('2112.13603v2-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 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.07840">arXiv:2112.07840</a> <span> [<a href="https://arxiv.org/pdf/2112.07840">pdf</a>, <a href="https://arxiv.org/format/2112.07840">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"> A Predictive Online Transient Stability Assessment with Hierarchical Generative Adversarial Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ma%2C+R">Rui Ma</a>, <a href="/search/eess?searchtype=author&query=Eftekharnejad%2C+S">Sara Eftekharnejad</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">Mustafa Cenk Gursoy</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="2112.07840v1-abstract-short" style="display: inline;"> Online transient stability assessment (TSA) is essential for secure and stable power system operations. The growing number of Phasor Measurement Units (PMUs) brings about massive sources of data that can enhance online TSA. However, conventional data-driven methods require large amounts of transient data to correctly assess the transient stability state of a system. In this paper, a new data-drive… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07840v1-abstract-full').style.display = 'inline'; document.getElementById('2112.07840v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.07840v1-abstract-full" style="display: none;"> Online transient stability assessment (TSA) is essential for secure and stable power system operations. The growing number of Phasor Measurement Units (PMUs) brings about massive sources of data that can enhance online TSA. However, conventional data-driven methods require large amounts of transient data to correctly assess the transient stability state of a system. In this paper, a new data-driven TSA approach is developed for TSA with fewer data compared to the conventional methods. The data reduction is enabled by learning the dynamic behaviors of the historical transient data using generative and adversarial networks (GAN). This knowledge is used online to predict the voltage time series data after a transient event. A classifier embedded in the generative network deploys the predicted post-contingency data to determine the stability of the system following a fault. The developed GAN-based TSA approach preserves the spatial and temporal correlations that exist in multivariate PMU time series data. Hence, in comparison with the state-of-the-art TSA methods, it achieves a higher assessment accuracy using only one sample of the measured data and a shorter response time. Case studies conducted on the IEEE 118-bus system demonstrate the superior performance of the GAN-based method compared to the conventional data-driven techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.07840v1-abstract-full').style.display = 'none'; document.getElementById('2112.07840v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.04912">arXiv:2112.04912</a> <span> [<a href="https://arxiv.org/pdf/2112.04912">pdf</a>, <a href="https://arxiv.org/ps/2112.04912">ps</a>, <a href="https://arxiv.org/format/2112.04912">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Joseph%2C+G">Geethu Joseph</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">M. Cenk Gursoy</a>, <a href="/search/eess?searchtype=author&query=Velipasalar%2C+S">Senem Velipasalar</a>, <a href="/search/eess?searchtype=author&query=Varshney%2C+P+K">Pramod K. Varshney</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="2112.04912v1-abstract-short" style="display: inline;"> We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04912v1-abstract-full').style.display = 'inline'; document.getElementById('2112.04912v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.04912v1-abstract-full" style="display: none;"> We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes. The objective of the detection algorithm is to identify the anomalies with an accuracy exceeding the desired value while minimizing the delay in decision making. We devise a centralized algorithm where the processes are jointly selected by a common agent as well as a decentralized algorithm where the decision of whether to select a process is made independently for each process. Our algorithms rely on a Markov decision process defined using the marginal probability of each process being normal or anomalous, conditioned on the observations. We implement the detection algorithms using the deep actor-critic reinforcement learning framework. Unlike prior work on this topic that has exponential complexity in the number of processes, our algorithms have computational and memory requirements that are both polynomial in the number of processes. We demonstrate the efficacy of these algorithms using numerical experiments by comparing them with state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.04912v1-abstract-full').style.display = 'none'; document.getElementById('2112.04912v1-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 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </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, 4 figures. arXiv admin note: substantial text overlap with arXiv:2105.06289</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2112.02615">arXiv:2112.02615</a> <span> [<a href="https://arxiv.org/pdf/2112.02615">pdf</a>, <a href="https://arxiv.org/ps/2112.02615">ps</a>, <a href="https://arxiv.org/format/2112.02615">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"> C-GRBFnet: A Physics-Inspired Generative Deep Neural Network for Channel Representation and Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhuoran Xiao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang Zhang</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+C">Chongwen Huang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Debbah%2C+M">M茅rouane Debbah</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="2112.02615v1-abstract-short" style="display: inline;"> In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user's position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional d… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02615v1-abstract-full').style.display = 'inline'; document.getElementById('2112.02615v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2112.02615v1-abstract-full" style="display: none;"> In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user's position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2112.02615v1-abstract-full').style.display = 'none'; document.getElementById('2112.02615v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 December, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.02911">arXiv:2109.02911</a> <span> [<a href="https://arxiv.org/pdf/2109.02911">pdf</a>, <a href="https://arxiv.org/ps/2109.02911">ps</a>, <a href="https://arxiv.org/format/2109.02911">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"> Exploiting Simultaneous Low-Rank and Sparsity in Delay-Angular Domain for Millimeter-Wave/Terahertz Wideband Massive Access </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Shao%2C+X">Xiaodan Shao</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2109.02911v1-abstract-short" style="display: inline;"> Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.02911v1-abstract-full').style.display = 'inline'; document.getElementById('2109.02911v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.02911v1-abstract-full" style="display: none;"> Millimeter-wave (mmW)/Terahertz (THz) wideband communication employing a large-scale antenna array is a promising technique of the sixth-generation (6G) wireless network for realizing massive machine-type communications (mMTC). To reduce the access latency and the signaling overhead, we design a grant-free random access scheme based on joint active device detection and channel estimation (JADCE) for mmW/THz wideband massive access. In particular, by exploiting the simultaneously sparse and low-rank structure of mmW/THz channels with spreads in the delay-angular domain, we propose two multi-rank aware JADCE algorithms via applying the quotient geometry of product of complex rank-$L$ matrices with the number of clusters $L$. It is proved that the proposed algorithms require a smaller number of measurements than the currently known bounds on measurements of conventional simultaneously sparse and low-rank recovery algorithms. Statistical analysis also shows that the proposed algorithms can linearly converge to the ground truth with low computational complexity. Finally, extensive simulation results confirm the superiority of the proposed algorithms in terms of the accuracy of both activity detection and channel estimation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.02911v1-abstract-full').style.display = 'none'; document.getElementById('2109.02911v1-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 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </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">IEEE Transactions on Wireless Communications, 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.09430">arXiv:2108.09430</a> <span> [<a href="https://arxiv.org/pdf/2108.09430">pdf</a>, <a href="https://arxiv.org/ps/2108.09430">ps</a>, <a href="https://arxiv.org/format/2108.09430">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"> An Attention-Aided Deep Learning Framework for Massive MIMO Channel Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gao%2C+J">Jiabao Gao</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+M">Mu Hu</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+G+Y">Geoffrey Ye Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2108.09430v1-abstract-short" style="display: inline;"> Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accurac… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09430v1-abstract-full').style.display = 'inline'; document.getElementById('2108.09430v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.09430v1-abstract-full" style="display: none;"> Channel estimation is one of the key issues in practical massive multiple-input multiple-output (MIMO) systems. Compared with conventional estimation algorithms, deep learning (DL) based ones have exhibited great potential in terms of performance and complexity. In this paper, an attention mechanism, exploiting the channel distribution characteristics, is proposed to improve the estimation accuracy of highly separable channels with narrow angular spread by realizing the "divide-and-conquer" policy. Specifically, we introduce a novel attention-aided DL channel estimation framework for conventional massive MIMO systems and devise an embedding method to effectively integrate the attention mechanism into the fully connected neural network for the hybrid analog-digital (HAD) architecture. Simulation results show that in both scenarios, the channel estimation performance is significantly improved with the aid of attention at the cost of small complexity overhead. Furthermore, strong robustness under different system and channel parameters can be achieved by the proposed approach, which further strengthens its practical value. We also investigate the distributions of learned attention maps to reveal the role of attention, which endows the proposed approach with a certain degree of interpretability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09430v1-abstract-full').style.display = 'none'; document.getElementById('2108.09430v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.00589">arXiv:2108.00589</a> <span> [<a href="https://arxiv.org/pdf/2108.00589">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <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/10.1177/0309524X211035153">10.1177/0309524X211035153 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Frequency support Scheme based on parametrized power curve for de-loaded Wind Turbine under various wind speed </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+Y">Yueming Lv</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Huayi Li</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">JiKai Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang 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="2108.00589v1-abstract-short" style="display: inline;"> With increased wind power penetration in modern power systems, wind plants are required to provide frequency support similar to conventional plants. However, for the existing frequency regulation scheme of wind turbines, the control gains in the auxiliary frequency controller are difficult to set because of the compromise of the frequency regulation performance and the stable operation of wind tur… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00589v1-abstract-full').style.display = 'inline'; document.getElementById('2108.00589v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.00589v1-abstract-full" style="display: none;"> With increased wind power penetration in modern power systems, wind plants are required to provide frequency support similar to conventional plants. However, for the existing frequency regulation scheme of wind turbines, the control gains in the auxiliary frequency controller are difficult to set because of the compromise of the frequency regulation performance and the stable operation of wind turbines, especially when the wind speed remains variable. This paper proposes a novel frequency regulation scheme (FRS) for de-loaded wind turbines. Instead of an auxiliary frequency controller, frequency support is provided by modifying the parametrized power versus rotor speed curve, including the inertia power versus rotor speed curve and the droop power versus rotor speed curve. The advantage of the proposed scheme is that it does not contain any control gains and generally adapts to different wind speeds. Further, the proposed scheme can work for the whole section of wind speed without wind speed measurement information. The compared simulation results demonstrate the scheme improves the system frequency response while ensuring the stable operation of doubly-fed induction generators (DFIGs)-based variable-speed wind turbines (VSWTs) under various wind conditions. Furthermore, the scheme prevents rotor speed overdeceleration even when the wind speed decreases during frequency regulation control. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.00589v1-abstract-full').style.display = 'none'; document.getElementById('2108.00589v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </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 Wind Engineering</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Wind Engineering 46 (2022) 459-479 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.11588">arXiv:2107.11588</a> <span> [<a href="https://arxiv.org/pdf/2107.11588">pdf</a>, <a href="https://arxiv.org/format/2107.11588">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="Distributed, Parallel, and Cluster Computing">cs.DC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <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"> Accelerating Federated Edge Learning via Optimized Probabilistic Device Scheduling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Maojun Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+G">Guangxu Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shuai Wang</a>, <a href="/search/eess?searchtype=author&query=Jiang%2C+J">Jiamo Jiang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Cui%2C+S">Shuguang Cui</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2107.11588v1-abstract-short" style="display: inline;"> The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.11588v1-abstract-full').style.display = 'inline'; document.getElementById('2107.11588v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.11588v1-abstract-full" style="display: none;"> The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.11588v1-abstract-full').style.display = 'none'; document.getElementById('2107.11588v1-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 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </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">In Proc. IEEE SPAWC2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.09842">arXiv:2107.09842</a> <span> [<a href="https://arxiv.org/pdf/2107.09842">pdf</a>, <a href="https://arxiv.org/format/2107.09842">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"> Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yao Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jiawei Yang</a>, <a href="/search/eess?searchtype=author&query=Tian%2C+J">Jiang Tian</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+Z">Zhongchao Shi</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yang Zhang</a>, <a href="/search/eess?searchtype=author&query=He%2C+Z">Zhiqiang He</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="2107.09842v1-abstract-short" style="display: inline;"> Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for the diagnosis of liver cancer. In this paper, we focus on improving automated liver tumor segmentation by integrating multi-modal CT images. To this end, we propose a novel mutual learning (ML) strategy for effective and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09842v1-abstract-full').style.display = 'inline'; document.getElementById('2107.09842v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.09842v1-abstract-full" style="display: none;"> Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for the diagnosis of liver cancer. In this paper, we focus on improving automated liver tumor segmentation by integrating multi-modal CT images. To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation. Different from existing multi-modal methods that fuse information from different modalities by a single model, with ML, an ensemble of modality-specific models learn collaboratively and teach each other to distill both the characteristics and the commonality between high-level representations of different modalities. The proposed ML not only enables the superiority for multi-modal learning but can also handle missing modalities by transferring knowledge from existing modalities to missing ones. Additionally, we present a modality-aware (MA) module, where the modality-specific models are interconnected and calibrated with attention weights for adaptive information exchange. The proposed modality-aware mutual learning (MAML) method achieves promising results for liver tumor segmentation on a large-scale clinical dataset. Moreover, we show the efficacy and robustness of MAML for handling missing modalities on both the liver tumor and public brain tumor (BRATS 2018) datasets. Our code is available at https://github.com/YaoZhang93/MAML. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.09842v1-abstract-full').style.display = 'none'; document.getElementById('2107.09842v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2107.01560">arXiv:2107.01560</a> <span> [<a href="https://arxiv.org/pdf/2107.01560">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="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/10.1016/j.ijepes.2021.107343">10.1016/j.ijepes.2021.107343 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Virtual synchronous generator of PV generation without energy storage for frequency support in autonomous microgrid </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Li%2C+H">Huayi Li</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+Y">Yang Zhou</a>, <a href="/search/eess?searchtype=author&query=Lv%2C+Y">Yueming Lv</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jikai Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yang 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="2107.01560v1-abstract-short" style="display: inline;"> In autonomous microgrids frequency regulation (FR) is a critical issue, especially with a high level of penetration of the photovoltaic (PV) generation. In this study, a novel virtual synchronous generator (VSG) control for PV generation was introduced to provide frequency support without energy storage. PV generation reserve a part of the active power in accordance with the pre-defined power vers… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.01560v1-abstract-full').style.display = 'inline'; document.getElementById('2107.01560v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.01560v1-abstract-full" style="display: none;"> In autonomous microgrids frequency regulation (FR) is a critical issue, especially with a high level of penetration of the photovoltaic (PV) generation. In this study, a novel virtual synchronous generator (VSG) control for PV generation was introduced to provide frequency support without energy storage. PV generation reserve a part of the active power in accordance with the pre-defined power versus voltage curve. Based on the similarities of the synchronous generator power-angle characteristic curve and the PV array characteristic curve, PV voltage Vpv can be analogized to the power angle 未. An emulated governor (droop control) and the swing equation control is designed and applied to the DC-DC converter. PV voltage deviation is subsequently generated and the pre-defined power versus voltage curve is modified to provide the primary frequency and inertia support. A simulation model of an autonomous microgrid with PV, storage, and diesel generator was built. The feasibility and effectiveness of the proposed VSG strategy are examined under different operating conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.01560v1-abstract-full').style.display = 'none'; document.getElementById('2107.01560v1-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 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </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 International Journal of Electrical Power and Energy Systems</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Journal of Electrical Power & Energy Systems 134 (2022) 107343 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.14591">arXiv:2106.14591</a> <span> [<a href="https://arxiv.org/pdf/2106.14591">pdf</a>, <a href="https://arxiv.org/format/2106.14591">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"> ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yixin Wang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yang Zhang</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/eess?searchtype=author&query=Lin%2C+Z">Zihao Lin</a>, <a href="/search/eess?searchtype=author&query=Tian%2C+J">Jiang Tian</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Shi%2C+Z">Zhongchao Shi</a>, <a href="/search/eess?searchtype=author&query=Fan%2C+J">Jianping Fan</a>, <a href="/search/eess?searchtype=author&query=He%2C+Z">Zhiqiang He</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="2106.14591v2-abstract-short" style="display: inline;"> Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities to provide complementary morphological and physiopathologic information. However, missing modality commonly occurs due to image corruption, artifacts, different acquisition protocols or allergies to certain contrast age… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.14591v2-abstract-full').style.display = 'inline'; document.getElementById('2106.14591v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.14591v2-abstract-full" style="display: none;"> Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities to provide complementary morphological and physiopathologic information. However, missing modality commonly occurs due to image corruption, artifacts, different acquisition protocols or allergies to certain contrast agents in clinical practice. Though existing efforts demonstrate the possibility of a unified model for all missing situations, most of them perform poorly when more than one modality is missing. In this paper, we propose a novel Adversarial Co-training Network (ACN) to solve this issue, in which a series of independent yet related models are trained dedicated to each missing situation with significantly better results. Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities. Then, two unsupervised modules, i.e., entropy and knowledge adversarial learning modules are proposed to minimize the domain gap while enhancing prediction reliability and encouraging the alignment of latent representations, respectively. We also adapt modality-mutual information knowledge transfer learning to ACN to retain the rich mutual information among modalities. Extensive experiments on BraTS2018 dataset show that our proposed method significantly outperforms all state-of-the-art methods under any missing situation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.14591v2-abstract-full').style.display = 'none'; document.getElementById('2106.14591v2-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 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </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 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2105.06288">arXiv:2105.06288</a> <span> [<a href="https://arxiv.org/pdf/2105.06288">pdf</a>, <a href="https://arxiv.org/ps/2105.06288">ps</a>, <a href="https://arxiv.org/format/2105.06288">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"> Anomaly Detection via Controlled Sensing and Deep Active Inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Joseph%2C+G">Geethu Joseph</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">M. Cenk Gursoy</a>, <a href="/search/eess?searchtype=author&query=Velipasalar%2C+S">Senem Velipasalar</a>, <a href="/search/eess?searchtype=author&query=Varshney%2C+P+K">Pramod K. Varshney</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="2105.06288v1-abstract-short" style="display: inline;"> In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.06288v1-abstract-full').style.display = 'inline'; document.getElementById('2105.06288v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2105.06288v1-abstract-full" style="display: none;"> In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe the processes until it obtains a sufficient number of measurements to reliably identify the anomalous processes. In this context, we develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies with an accuracy exceeding a desired value while minimizing the delay in making the decision and the total number of measurements taken. Our algorithm is based on active inference which is a general framework to make sequential decisions in order to maximize the notion of free energy. We define the free energy using the objectives of the selection policy and implement the active inference framework using a deep neural network approximation. Using numerical experiments, we compare our algorithm with the state-of-the-art method based on deep actor-critic reinforcement learning and demonstrate the superior performance of our algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2105.06288v1-abstract-full').style.display = 'none'; document.getElementById('2105.06288v1-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 May, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">6 pages,9 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Globecom 2020 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.12817">arXiv:2102.12817</a> <span> [<a href="https://arxiv.org/pdf/2102.12817">pdf</a>, <a href="https://arxiv.org/ps/2102.12817">ps</a>, <a href="https://arxiv.org/format/2102.12817">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"> Fronthaul Compression and Passive Beamforming Design for Intelligent Reflecting Surface-aided Cloud Radio Access Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yu Zhang</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+X">Xuelu Wu</a>, <a href="/search/eess?searchtype=author&query=Peng%2C+H">Hong Peng</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</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="2102.12817v2-abstract-short" style="display: inline;"> This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH). Specifically, we consider the uplink transmission where each RRH quantizes the received signals from the users by either point-to-point compression or Wyner-Ziv compression and then transmits the quantization bits to the BBU pool through… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.12817v2-abstract-full').style.display = 'inline'; document.getElementById('2102.12817v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.12817v2-abstract-full" style="display: none;"> This letter studies a cloud radio access network (C-RAN) with multiple intelligent reflecting surfaces (IRS) deployed between users and remote radio heads (RRH). Specifically, we consider the uplink transmission where each RRH quantizes the received signals from the users by either point-to-point compression or Wyner-Ziv compression and then transmits the quantization bits to the BBU pool through capacity limited fronthhual links. To maximize the uplink sum rate, we jointly optimize the passive beamformers of IRSs and the quantization noise covariance matrices of fronthoul compression. An joint fronthaul compression and passive beamforming design is proposed by exploiting the Arimoto-Blahut algorithm and semidefinte relaxation (SDR). Numerical results show the performance gain achieved by the proposed algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.12817v2-abstract-full').style.display = 'none'; document.getElementById('2102.12817v2-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 April, 2021; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2012.14785">arXiv:2012.14785</a> <span> [<a href="https://arxiv.org/pdf/2012.14785">pdf</a>, <a href="https://arxiv.org/format/2012.14785">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"> Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yao Zhang</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+J">Jiawei Yang</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+F">Feng Hou</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yixin Wang</a>, <a href="/search/eess?searchtype=author&query=Tian%2C+J">Jiang Tian</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yang Zhang</a>, <a href="/search/eess?searchtype=author&query=He%2C+Z">Zhiqiang He</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="2012.14785v2-abstract-short" style="display: inline;"> Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However, the shortage of annotation and the variance of the data among different vendors and medical centers restrict the performance of advanced deep learning methods. In this work, we present a fully automatic method to segment… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.14785v2-abstract-full').style.display = 'inline'; document.getElementById('2012.14785v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2012.14785v2-abstract-full" style="display: none;"> Accurate segmentation of cardiac structures can assist doctors to diagnose diseases, and to improve treatment planning, which is highly demanded in the clinical practice. However, the shortage of annotation and the variance of the data among different vendors and medical centers restrict the performance of advanced deep learning methods. In this work, we present a fully automatic method to segment cardiac structures including the left (LV) and right ventricle (RV) blood pools, as well as for the left ventricular myocardium (MYO) in MRI volumes. Specifically, we design a semi-supervised learning method to leverage unlabelled MRI sequence timeframes by label propagation. Then we exploit style transfer to reduce the variance among different centers and vendors for more robust cardiac image segmentation. We evaluate our method in the M&Ms challenge 7 , ranking 2nd place among 14 competitive teams. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2012.14785v2-abstract-full').style.display = 'none'; document.getElementById('2012.14785v2-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 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The method that won 2nd place in MICCAI 2020 MnM's Challenge</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.09277">arXiv:2010.09277</a> <span> [<a href="https://arxiv.org/pdf/2010.09277">pdf</a>, <a href="https://arxiv.org/format/2010.09277">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"> Modality-Pairing Learning for Brain Tumor Segmentation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yixin Wang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yao Zhang</a>, <a href="/search/eess?searchtype=author&query=Hou%2C+F">Feng Hou</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/eess?searchtype=author&query=Tian%2C+J">Jiang Tian</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Cheng Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yang Zhang</a>, <a href="/search/eess?searchtype=author&query=He%2C+Z">Zhiqiang He</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="2010.09277v2-abstract-short" style="display: inline;"> Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment of brain tumor. However, previous methods mostly ignore the latent relationship among different modalities. In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation. Paral… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09277v2-abstract-full').style.display = 'inline'; document.getElementById('2010.09277v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.09277v2-abstract-full" style="display: none;"> Automatic brain tumor segmentation from multi-modality Magnetic Resonance Images (MRI) using deep learning methods plays an important role in assisting the diagnosis and treatment of brain tumor. However, previous methods mostly ignore the latent relationship among different modalities. In this work, we propose a novel end-to-end Modality-Pairing learning method for brain tumor segmentation. Paralleled branches are designed to exploit different modality features and a series of layer connections are utilized to capture complex relationships and abundant information among modalities. We also use a consistency loss to minimize the prediction variance between two branches. Besides, learning rate warmup strategy is adopted to solve the problem of the training instability and early over-fitting. Lastly, we use average ensemble of multiple models and some post-processing techniques to get final results. Our method is tested on the BraTS 2020 online testing dataset, obtaining promising segmentation performance, with average dice scores of 0.891, 0.842, 0.816 for the whole tumor, tumor core and enhancing tumor, respectively. We won the second place of the BraTS 2020 Challenge for the tumor segmentation task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.09277v2-abstract-full').style.display = 'none'; document.getElementById('2010.09277v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 December, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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">Second place of BraTS 2020 Challenge</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.00644">arXiv:2010.00644</a> <span> [<a href="https://arxiv.org/pdf/2010.00644">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optics">physics.optics</span> <span class="tag is-small is-grey 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="Biological Physics">physics.bio-ph</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1364/OL.413849">10.1364/OL.413849 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Single-objective selective-volume illumination microscopy enables high-contrast light-field imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Madaan%2C+S">Sara Madaan</a>, <a href="/search/eess?searchtype=author&query=Keomanee-Dizon%2C+K">Kevin Keomanee-Dizon</a>, <a href="/search/eess?searchtype=author&query=Jones%2C+M">Matt Jones</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chenyang Zhong</a>, <a href="/search/eess?searchtype=author&query=Nadtochiy%2C+A">Anna Nadtochiy</a>, <a href="/search/eess?searchtype=author&query=Luu%2C+P">Peter Luu</a>, <a href="/search/eess?searchtype=author&query=Fraser%2C+S+E">Scott E. Fraser</a>, <a href="/search/eess?searchtype=author&query=Truong%2C+T+V">Thai V. Truong</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="2010.00644v2-abstract-short" style="display: inline;"> The performance of light-field microscopy is improved by selectively illuminating the relevant subvolume of the specimen with a second objective lens [1-3]. Here we advance this approach to a single-objective geometry, using an oblique one-photon illumination path or two-photon illumination to accomplish selective-volume excitation. The elimination of the second orthogonally oriented objective to… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.00644v2-abstract-full').style.display = 'inline'; document.getElementById('2010.00644v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.00644v2-abstract-full" style="display: none;"> The performance of light-field microscopy is improved by selectively illuminating the relevant subvolume of the specimen with a second objective lens [1-3]. Here we advance this approach to a single-objective geometry, using an oblique one-photon illumination path or two-photon illumination to accomplish selective-volume excitation. The elimination of the second orthogonally oriented objective to selectively excite the volume of interest simplifies specimen mounting; yet, this single-objective approach still reduces out-of-volume background, resulting in improvements in image contrast, effective resolution, and volume reconstruction quality. We validate our new approach through imaging live developing zebrafish, demonstrating the technology's ability to capture imaging data from large volumes synchronously with high contrast, while remaining compatible with standard microscope sample mounting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.00644v2-abstract-full').style.display = 'none'; document.getElementById('2010.00644v2-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, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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">Sara Madaan and Kevin Keomanee-Dizon contributed equally to this work; 4 pages, 3 figures; supplementary material included</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2009.00274">arXiv:2009.00274</a> <span> [<a href="https://arxiv.org/pdf/2009.00274">pdf</a>, <a href="https://arxiv.org/ps/2009.00274">ps</a>, <a href="https://arxiv.org/format/2009.00274">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"> Intelligent Reflecting Surface Aided Multicasting with Random Passive Beamforming </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Tao%2C+Q">Qin Tao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shuowen Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2009.00274v1-abstract-short" style="display: inline;"> In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with $N$ passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI req… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.00274v1-abstract-full').style.display = 'inline'; document.getElementById('2009.00274v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2009.00274v1-abstract-full" style="display: none;"> In this letter, we consider a multicast system where a single-antenna transmitter sends a common message to multiple single-antenna users, aided by an intelligent reflecting surface (IRS) equipped with $N$ passive reflecting elements. Prior works on IRS have mostly assumed the availability of channel state information (CSI) for designing its passive beamforming. However, the acquisition of CSI requires substantial training overhead that increases with $N$. In contrast, we propose in this letter a novel \emph{random passive beamforming} scheme, where the IRS performs independent random reflection for $Q\geq 1$ times in each channel coherence interval without the need of CSI acquisition. For the proposed scheme, we first derive a closed-form approximation of the outage probability, based on which the optimal $Q$ with best outage performance can be efficiently obtained. Then, for the purpose of comparison, we derive a lower bound of the outage probability with traditional CSI-based passive beamforming. Numerical results show that a small $Q$ is preferred in the high-outage regime (or with high rate target) and the optimal $Q$ becomes larger as the outage probability decreases (or as the rate target decreases). Moreover, the proposed scheme significantly outperforms the CSI-based passive beamforming scheme with training overhead taken into consideration when $N$ and/or the number of users are large, thus offering a promising CSI-free alternative to existing CSI-based schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2009.00274v1-abstract-full').style.display = 'none'; document.getElementById('2009.00274v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 September, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in IEEE Wireless Communications Letter</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> C.2.1 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> C.2.1 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.11864">arXiv:2008.11864</a> <span> [<a href="https://arxiv.org/pdf/2008.11864">pdf</a>, <a href="https://arxiv.org/ps/2008.11864">ps</a>, <a href="https://arxiv.org/format/2008.11864">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"> Robust Design for IRS-Aided Communication Systems with User Location Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Alouini%2C+M">Mohamed-Slim Alouini</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2008.11864v1-abstract-short" style="display: inline;"> In this paper, we propose a robust design framework for IRS-aided communication systems in the presence of user location uncertainty. By jointly designing the transmit beamforming vector at the BS and phase shifts at the IRS, we aim to minimize the transmit power subject to the worse-case quality of service (QoS) constraint, i.e., ensuring the user rate is above a threshold for all possible user l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.11864v1-abstract-full').style.display = 'inline'; document.getElementById('2008.11864v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.11864v1-abstract-full" style="display: none;"> In this paper, we propose a robust design framework for IRS-aided communication systems in the presence of user location uncertainty. By jointly designing the transmit beamforming vector at the BS and phase shifts at the IRS, we aim to minimize the transmit power subject to the worse-case quality of service (QoS) constraint, i.e., ensuring the user rate is above a threshold for all possible user location error realizations. With unit-modulus, this problem is not convex. The location uncertainty in the QoS constraint further increases the difficulty of solving this problem. By utilizing techniques of Taylor expansion, S-Procedure and semidefinite relaxation (SDP), we transform this problem into a sequence of semidefinite programming (SDP) sub-problems. Simulation results show that the proposed robust algorithm substantially outperforms the non-robust algorithm proposed in the literature, in terms of probability of reaching the required QoS target. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.11864v1-abstract-full').style.display = 'none'; document.getElementById('2008.11864v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in IEEE Wireless Communications Letters</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.09248">arXiv:2008.09248</a> <span> [<a href="https://arxiv.org/pdf/2008.09248">pdf</a>, <a href="https://arxiv.org/ps/2008.09248">ps</a>, <a href="https://arxiv.org/format/2008.09248">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"> Location Information Aided Multiple Intelligent Reflecting Surface Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yu Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2008.09248v1-abstract-short" style="display: inline;"> This paper proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achie… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.09248v1-abstract-full').style.display = 'inline'; document.getElementById('2008.09248v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.09248v1-abstract-full" style="display: none;"> This paper proposes a novel location information aided multiple intelligent reflecting surface (IRS) systems. Assuming imperfect user location information, the effective angles from the IRS to the users are estimated, which is then used to design the transmit beam and IRS beam. Furthermore, closed-form expressions for the achievable rate are derived. The analytical findings indicate that the achievable rate can be improved by increasing the number of base station (BS) antennas or reflecting elements. Specifically, a power gain of order $N M^2$ is achieved, where $N$ is the antenna number and $M$ is the number of reflecting elements. Moreover, with a large number of reflecting elements, the individual signal to interference plus noise ratio (SINR) is proportional to $M$, while becomes proportional to $M^2$ as non-line-of-sight (NLOS) paths vanish. Also, it has been shown that high location uncertainty would significantly degrade the achievable rate. Besides, IRSs should be deployed at distinct directions (relative to the BS) and be far away from each other to reduce the interference from multiple IRSs. Finally, an optimal power allocation scheme has been proposed to improve the system performance. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.09248v1-abstract-full').style.display = 'none'; document.getElementById('2008.09248v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </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 to appear in IEEEE Transactions on Communications</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.08453">arXiv:2008.08453</a> <span> [<a href="https://arxiv.org/pdf/2008.08453">pdf</a>, <a href="https://arxiv.org/ps/2008.08453">ps</a>, <a href="https://arxiv.org/format/2008.08453">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"> Statistical CSI based Design for Intelligent Reflecting Surface Assisted MISO Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+X">Xiaoling Hu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+J">Junwei Wang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2008.08453v1-abstract-short" style="display: inline;"> This paper considers an intelligent reflecting surface (IRS) aided multiple-input single-output communication system, where statistical channel state information (CSI) is exploited for transmit beamforming and IRS beamforming. A tight upper bound is derived for the ergodic capacity of the system. Based on which, the joint optimization of transmit beam and IRS beam are studied. Depending on whether… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.08453v1-abstract-full').style.display = 'inline'; document.getElementById('2008.08453v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.08453v1-abstract-full" style="display: none;"> This paper considers an intelligent reflecting surface (IRS) aided multiple-input single-output communication system, where statistical channel state information (CSI) is exploited for transmit beamforming and IRS beamforming. A tight upper bound is derived for the ergodic capacity of the system. Based on which, the joint optimization of transmit beam and IRS beam are studied. Depending on whether a line-of-sight path exists between the access point and user, two different cases, namely, Rician fading and Rayleigh fading, are separately treated. Specifically, for the Rician fading case, an iterative algorithm is proposed, which is guaranteed to converge. For the Rayleigh fading case, closed-form designs are obtained for the transmit beam and IRS beam. Simulation results show the proposed beamforming scheme achieves similar performance as the benchmark algorithm requiring instantaneous CSI. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.08453v1-abstract-full').style.display = 'none'; document.getElementById('2008.08453v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 19 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </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 to appear in Science China: Information Science</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2008.03977">arXiv:2008.03977</a> <span> [<a href="https://arxiv.org/pdf/2008.03977">pdf</a>, <a href="https://arxiv.org/ps/2008.03977">ps</a>, <a href="https://arxiv.org/format/2008.03977">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/LCOMM.2020.3014382">10.1109/LCOMM.2020.3014382 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Deep Learning for Joint Channel Estimation and Signal Detection in OFDM Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yi%2C+X">Xuemei Yi</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</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="2008.03977v1-abstract-short" style="display: inline;"> In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03977v1-abstract-full').style.display = 'inline'; document.getElementById('2008.03977v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.03977v1-abstract-full" style="display: none;"> In this paper, we propose a novel deep learning based approach for joint channel estimation and signal detection in orthogonal frequency division multiplexing (OFDM) systems by exploring the time and frequency correlation of wireless fading channels. Specifically, a Channel Estimation Network (CENet) is designed to replace the conventional interpolation procedure in pilot-aided estimation scheme. Then, based on the outcome of the CENet, a Channel Conditioned Recovery Network (CCRNet) is designed to recover the transmit signal. Experimental results demonstrate that CENet and CCRNet achieve superior performance compared with conventional estimation and detection methods. In addition, both networks are shown to be robust to the variation of parameter chances, which makes them appealing for practical implementation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03977v1-abstract-full').style.display = 'none'; document.getElementById('2008.03977v1-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 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in IEEE Communications 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/2008.03868">arXiv:2008.03868</a> <span> [<a href="https://arxiv.org/pdf/2008.03868">pdf</a>, <a href="https://arxiv.org/ps/2008.03868">ps</a>, <a href="https://arxiv.org/format/2008.03868">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"> Robust Design for NOMA-based Multi-Beam LEO Satellite Internet of Things </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chu%2C+J">Jianhang Chu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+X">Xiaoming Chen</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Caijun Zhong</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zhaoyang 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="2008.03868v1-abstract-short" style="display: inline;"> In this paper, we investigate the issue of massive access in a beyond fifth-generation (B5G) multi-beam low earth orbit (LEO) satellite internet of things (IoT) network in the presence of channel phase uncertainty due to channel state information (CSI) conveyance from the devices to the satellite via the gateway. Rather than time division multiple access (TDMA) or frequency division multiple acces… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03868v1-abstract-full').style.display = 'inline'; document.getElementById('2008.03868v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2008.03868v1-abstract-full" style="display: none;"> In this paper, we investigate the issue of massive access in a beyond fifth-generation (B5G) multi-beam low earth orbit (LEO) satellite internet of things (IoT) network in the presence of channel phase uncertainty due to channel state information (CSI) conveyance from the devices to the satellite via the gateway. Rather than time division multiple access (TDMA) or frequency division multiple access (FDMA) with multi-color pattern, a new non-orthogonal multiple access (NOMA) scheme is adopted to support massive IoT distributed over a very wide range. Considering the limited energy on the LEO satellite, two robust beamforming algorithms against channel phase uncertainty are proposed for minimizing the total power consumption in the scenarios of noncritical IoT applications and critical IoT applications, respectively. Both thoeretical analysis and simulation results validate the effectiveness and robustness of the proposed algorithms for supporting massive access in satellite IoT. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2008.03868v1-abstract-full').style.display = 'none'; document.getElementById('2008.03868v1-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 August, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2007.06055">arXiv:2007.06055</a> <span> [<a href="https://arxiv.org/pdf/2007.06055">pdf</a>, <a href="https://arxiv.org/format/2007.06055">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> <div 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/ACCESS.2021.3133506">10.1109/ACCESS.2021.3133506 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Adversarial jamming attacks and defense strategies via adaptive deep reinforcement learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+F">Feng Wang</a>, <a href="/search/eess?searchtype=author&query=Zhong%2C+C">Chen Zhong</a>, <a href="/search/eess?searchtype=author&query=Gursoy%2C+M+C">M. Cenk Gursoy</a>, <a href="/search/eess?searchtype=author&query=Velipasalar%2C+S">Senem Velipasalar</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="2007.06055v1-abstract-short" style="display: inline;"> As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper consider a victim user that performs DRL-based dynamic channel access, and an at… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06055v1-abstract-full').style.display = 'inline'; document.getElementById('2007.06055v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2007.06055v1-abstract-full" style="display: none;"> As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention. In order to address such sensitivity and alleviate the resulting security concerns, we in this paper consider a victim user that performs DRL-based dynamic channel access, and an attacker that executes DRLbased jamming attacks to disrupt the victim. Hence, both the victim and attacker are DRL agents and can interact with each other, retrain their models, and adapt to opponents' policies. In this setting, we initially develop an adversarial jamming attack policy that aims at minimizing the accuracy of victim's decision making on dynamic channel access. Subsequently, we devise defense strategies against such an attacker, and propose three defense strategies, namely diversified defense with proportional-integral-derivative (PID) control, diversified defense with an imitation attacker, and defense via orthogonal policies. We design these strategies to maximize the attacked victim's accuracy and evaluate their performances. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2007.06055v1-abstract-full').style.display = 'none'; document.getElementById('2007.06055v1-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 July, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2020. </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, 24 figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 (Primary) 94A15 (Secondary) </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" 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