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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.21165">arXiv:2503.21165</a> <span> [<a href="https://arxiv.org/pdf/2503.21165">pdf</a>, <a href="https://arxiv.org/format/2503.21165">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Babu%2C+S+J">Shaik Jani Babu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+F">Fan Hu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Singhal%2C+S">Sonal Singhal</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+X">Xinfei Guo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.21165v1-abstract-short" style="display: inline;"> Reliability has become an increasing concern in modern computing. Integrated circuits (ICs) are the backbone of modern computing devices across industries, including artificial intelligence (AI), consumer electronics, healthcare, automotive, industrial, and aerospace. Moore Law has driven the semiconductor IC industry toward smaller dimensions, improved performance, and greater energy efficiency.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.21165v1-abstract-full').style.display = 'inline'; document.getElementById('2503.21165v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.21165v1-abstract-full" style="display: none;"> Reliability has become an increasing concern in modern computing. Integrated circuits (ICs) are the backbone of modern computing devices across industries, including artificial intelligence (AI), consumer electronics, healthcare, automotive, industrial, and aerospace. Moore Law has driven the semiconductor IC industry toward smaller dimensions, improved performance, and greater energy efficiency. However, as transistors shrink to atomic scales, aging-related degradation mechanisms such as Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), Time-Dependent Dielectric Breakdown (TDDB), Electromigration (EM), and stochastic aging-induced variations have become major reliability threats. From an application perspective, applications like AI training and autonomous driving require continuous and sustainable operation to minimize recovery costs and enhance safety. Additionally, the high cost of chip replacement and reproduction underscores the need for extended lifespans. These factors highlight the urgency of designing more reliable ICs. This survey addresses the critical aging issues in ICs, focusing on fundamental degradation mechanisms and mitigation strategies. It provides a comprehensive overview of aging impact and the methods to counter it, starting with the root causes of aging and summarizing key monitoring techniques at both circuit and system levels. A detailed analysis of circuit-level mitigation strategies highlights the distinct aging characteristics of digital, analog, and SRAM circuits, emphasizing the need for tailored solutions. The survey also explores emerging software approaches in design automation, aging characterization, and mitigation, which are transforming traditional reliability optimization. Finally, it outlines the challenges and future directions for improving aging management and ensuring the long-term reliability of ICs across diverse applications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.21165v1-abstract-full').style.display = 'none'; document.getElementById('2503.21165v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This work is under review by ACM</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.20509">arXiv:2503.20509</a> <span> [<a href="https://arxiv.org/pdf/2503.20509">pdf</a>, <a href="https://arxiv.org/format/2503.20509">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"> Problem-Structure-Informed Quantum Approximate Optimization Algorithm for Large-Scale Unit Commitment with Limited Qubits </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhou%2C+J">Jingxian Zhou</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Ziqing Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linghua Zhu</a>, <a href="/search/eess?searchtype=author&query=Bu%2C+S">Siqi Bu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.20509v1-abstract-short" style="display: inline;"> As power systems expand, solving the Unit Commitment Problem (UCP) becomes increasingly challenging due to the dimensional catastrophe, and traditional methods often struggle to balance computational efficiency and solution quality. To tackle this issue, we propose a problem-structure-informed Quantum Approximate Optimization Algorithm (QAOA) framework that fully exploits the quantum advantage und… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20509v1-abstract-full').style.display = 'inline'; document.getElementById('2503.20509v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.20509v1-abstract-full" style="display: none;"> As power systems expand, solving the Unit Commitment Problem (UCP) becomes increasingly challenging due to the dimensional catastrophe, and traditional methods often struggle to balance computational efficiency and solution quality. To tackle this issue, we propose a problem-structure-informed Quantum Approximate Optimization Algorithm (QAOA) framework that fully exploits the quantum advantage under extremely limited quantum resources. Specifically, we leverage the inherent topological structure of power systems to decompose large-scale UCP instances into smaller subproblems, each solvable in parallel by limited number of qubits. This decomposition not only circumvents the current hardware limitations of quantum computing but also achieves higher performance as the graph structure of the power system becomes more sparse. Consequently, our approach can be readily extended to future power systems that are larger and more complex. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.20509v1-abstract-full').style.display = 'none'; document.getElementById('2503.20509v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 26 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.18240">arXiv:2503.18240</a> <span> [<a href="https://arxiv.org/pdf/2503.18240">pdf</a>, <a href="https://arxiv.org/format/2503.18240">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Tutorial on Six-Dimensional Movable Antenna Enhanced Wireless Networks: Synergizing Positionable and Rotatable Antennas </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=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=You%2C+C">Changsheng You</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Beixiong Zheng</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+C">Cheng-Xiang Wang</a>, <a href="/search/eess?searchtype=author&query=Li%2C+J">Junling Li</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+R">Rui Zhang</a>, <a href="/search/eess?searchtype=author&query=Schober%2C+R">Robert Schober</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhuang%2C+W">Weihua Zhuang</a>, <a href="/search/eess?searchtype=author&query=Xuemin"> Xuemin</a>, <a href="/search/eess?searchtype=author&query=Shen"> Shen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.18240v1-abstract-short" style="display: inline;"> Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively without the need to deploy addition… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18240v1-abstract-full').style.display = 'inline'; document.getElementById('2503.18240v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.18240v1-abstract-full" style="display: none;"> Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively without the need to deploy additional antennas. It is thus expected that the integration of new 6DMAs into future sixth-generation (6G) wireless networks will fundamentally enhance antenna agility and adaptability, and introduce new degrees of freedom (DoFs) for system design. Despite its great potential, 6DMA faces new challenges to be efficiently implemented in wireless networks, including corresponding architectures, antenna position and rotation optimization, channel estimation, and system design from both communication and sensing perspectives. In this paper, we provide a tutorial on 6DMA-enhanced wireless networks to address the above issues by unveiling associated new channel models, hardware implementations and practical position/rotation constraints, as well as various appealing applications in wireless networks. Moreover, we discuss two special cases of 6DMA, namely, rotatable 6DMA with fixed antenna position and positionable 6DMA with fixed antenna rotation, and highlight their respective design challenges and applications. We further present prototypes developed for 6DMA-enhanced communication along with experimental results obtained with these prototypes. Finally, we outline promising directions for further investigation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.18240v1-abstract-full').style.display = 'none'; document.getElementById('2503.18240v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> <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">46 pages, submitted to IEEE for publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.11321">arXiv:2503.11321</a> <span> [<a href="https://arxiv.org/pdf/2503.11321">pdf</a>, <a href="https://arxiv.org/format/2503.11321">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+X">Xiangrui Zeng</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+B">Bolin Chen</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+P">Peilin Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yung-Hui Li</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shiqi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.11321v1-abstract-short" style="display: inline;"> By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions produced by rate-distortion-optimized models. In this paper, we propose a novel deep learning-based generative image compression method injected with diffusion knowledge, obtaining the capacity to recover more realistic te… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.11321v1-abstract-full').style.display = 'inline'; document.getElementById('2503.11321v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.11321v1-abstract-full" style="display: none;"> By optimizing the rate-distortion-realism trade-off, generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions produced by rate-distortion-optimized models. In this paper, we propose a novel deep learning-based generative image compression method injected with diffusion knowledge, obtaining the capacity to recover more realistic textures in practical scenarios. Efforts are made from three perspectives to navigate the rate-distortion-realism trade-off in the generative image compression task. First, recognizing the strong connection between image texture and frequency-domain characteristics, we design a Fractal Frequency-Aware Band Image Compression (FFAB-IC) network to effectively capture the directional frequency components inherent in natural images. This network integrates commonly used fractal band feature operations within a neural non-linear mapping design, enhancing its ability to retain essential given information and filter out unnecessary details. Then, to improve the visual quality of image reconstruction under limited bandwidth, we integrate diffusion knowledge into the encoder and implement diffusion iterations into the decoder process, thus effectively recovering lost texture details. Finally, to fully leverage the spatial and frequency intensity information, we incorporate frequency- and content-aware regularization terms to regularize the training of the generative image compression network. Extensive experiments in quantitative and qualitative evaluations demonstrate the superiority of the proposed method, advancing the boundaries of achievable distortion-realism pairs, i.e., our method achieves better distortions at high realism and better realism at low distortion than ever before. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.11321v1-abstract-full').style.display = 'none'; document.getElementById('2503.11321v1-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 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2503.04563">arXiv:2503.04563</a> <span> [<a href="https://arxiv.org/pdf/2503.04563">pdf</a>, <a href="https://arxiv.org/format/2503.04563">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Occlusion-Aware Consistent Model Predictive Control for Robot Navigation in Occluded Obstacle-Dense Environments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zheng%2C+M">Minzhe Zheng</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+L">Lei Zheng</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lei Zhu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+J">Jun Ma</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2503.04563v1-abstract-short" style="display: inline;"> Ensuring safety and motion consistency for robot navigation in occluded, obstacle-dense environments is a critical challenge. In this context, this study presents an occlusion-aware Consistent Model Predictive Control (CMPC) strategy. To account for the occluded obstacles, it incorporates adjustable risk regions that represent their potential future locations. Subsequently, dynamic risk boundary c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04563v1-abstract-full').style.display = 'inline'; document.getElementById('2503.04563v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2503.04563v1-abstract-full" style="display: none;"> Ensuring safety and motion consistency for robot navigation in occluded, obstacle-dense environments is a critical challenge. In this context, this study presents an occlusion-aware Consistent Model Predictive Control (CMPC) strategy. To account for the occluded obstacles, it incorporates adjustable risk regions that represent their potential future locations. Subsequently, dynamic risk boundary constraints are developed online to ensure safety. The CMPC then constructs multiple locally optimal trajectory branches (each tailored to different risk regions) to balance between exploitation and exploration. A shared consensus trunk is generated to ensure smooth transitions between branches without significant velocity fluctuations, further preserving motion consistency. To facilitate high computational efficiency and ensure coordination across local trajectories, we use the alternating direction method of multipliers (ADMM) to decompose the CMPC into manageable sub-problems for parallel solving. The proposed strategy is validated through simulation and real-world experiments on an Ackermann-steering robot platform. The results demonstrate the effectiveness of the proposed CMPC strategy through comparisons with baseline approaches in occluded, obstacle-dense environments. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2503.04563v1-abstract-full').style.display = 'none'; document.getElementById('2503.04563v1-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 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.21036">arXiv:2502.21036</a> <span> [<a href="https://arxiv.org/pdf/2502.21036">pdf</a>, <a href="https://arxiv.org/format/2502.21036">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 Demo of Radar Sensing Aided Rotatable Antenna for Wireless Communication System </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dai%2C+Q">Qi Dai</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Beixiong Zheng</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Q">Qiyao Wang</a>, <a href="/search/eess?searchtype=author&query=Xiong%2C+X">Xue Xiong</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+X">Xiaodan Shao</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2502.21036v1-abstract-short" style="display: inline;"> Rotatable antenna (RA) represents a novel antenna architecture that enhances wireless communication system performance by independently or collectively adjusting each antenna's boresight/orientation. In this demonstration, we develop a prototype of radar sensing-aided rotatable antenna that integrates radar sensing with dynamic antenna orientation to enhance wireless communication performance whil… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.21036v1-abstract-full').style.display = 'inline'; document.getElementById('2502.21036v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.21036v1-abstract-full" style="display: none;"> Rotatable antenna (RA) represents a novel antenna architecture that enhances wireless communication system performance by independently or collectively adjusting each antenna's boresight/orientation. In this demonstration, we develop a prototype of radar sensing-aided rotatable antenna that integrates radar sensing with dynamic antenna orientation to enhance wireless communication performance while maintaining low hardware costs. The proposed prototype consists of a transmitter (TX) module and a receiver (RX) module, both of which employ universal software radio peripherals (USRPs) for transmitting and receiving signals. Specifically, the TX utilizes a laser radar to detect the RX's location and conveys the angle of arrival (AoA) information to its antenna servo, which enables the RA to align its boresight direction with the identified RX. Experimental results examine the effectiveness of the proposed prototype and indicate that the RA significantly outperforms the traditional fixed-antenna system in terms of increasing received signal-to-noise ratio (SNR). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.21036v1-abstract-full').style.display = 'none'; document.getElementById('2502.21036v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.20856">arXiv:2502.20856</a> <span> [<a href="https://arxiv.org/pdf/2502.20856">pdf</a>, <a href="https://arxiv.org/format/2502.20856">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"> Movable Antenna Aided Multiuser Communications: Antenna Position Optimization Based on Statistical Channel Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+G">Ge Yan</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2502.20856v1-abstract-short" style="display: inline;"> The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). However, the resulting real-time antenna mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.20856v1-abstract-full').style.display = 'inline'; document.getElementById('2502.20856v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.20856v1-abstract-full" style="display: none;"> The movable antenna (MA) technology has attracted great attention recently due to its promising capability in improving wireless channel conditions by flexibly adjusting antenna positions. To reap maximal performance gains of MA systems, existing works mainly focus on MA position optimization to cater to the instantaneous channel state information (CSI). However, the resulting real-time antenna movement may face challenges in practical implementation due to the additional time overhead and energy consumption required, especially in fast time-varying channel scenarios. To address this issue, we propose in this paper a new approach to optimize the MA positions based on the users' statistical CSI over a large timescale. In particular, we propose a general field response based statistical channel model to characterize the random channel variations caused by the local movement of users. Based on this model, a two-timescale optimization problem is formulated to maximize the ergodic sum rate of multiple users, where the precoding matrix and the positions of MAs at the base station (BS) are optimized based on the instantaneous and statistical CSI, respectively. To solve this non-convex optimization problem, a log-barrier penalized gradient ascent algorithm is developed to optimize the MA positions, where two methods are proposed to approximate the ergodic sum rate and its gradients with different complexities. Finally, we present simulation results to evaluate the performance of the proposed design and algorithms based on practical channels generated by ray-tracing. The results verify the performance advantages of MA systems compared to their fixed-position antenna (FPA) counterparts in terms of long-term rate improvement, especially for scenarios with more diverse channel power distributions in the angular domain. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.20856v1-abstract-full').style.display = 'none'; document.getElementById('2502.20856v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">16 pages, 14 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/2502.17905">arXiv:2502.17905</a> <span> [<a href="https://arxiv.org/pdf/2502.17905">pdf</a>, <a href="https://arxiv.org/format/2502.17905">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> A Tutorial on Movable Antennas for Wireless Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+W">Wenyan Ma</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Zeng%2C+Y">Yong Zeng</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+Q">Qingqing Wu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a>, <a href="/search/eess?searchtype=author&query=Shao%2C+X">Xiaodan Shao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+J">Jun Zhang</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="2502.17905v1-abstract-short" style="display: inline;"> Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17905v1-abstract-full').style.display = 'inline'; document.getElementById('2502.17905v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17905v1-abstract-full" style="display: none;"> Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17905v1-abstract-full').style.display = 'none'; document.getElementById('2502.17905v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publiation in the IEEE Communications Surveys & Tutorials</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17097">arXiv:2502.17097</a> <span> [<a href="https://arxiv.org/pdf/2502.17097">pdf</a>, <a href="https://arxiv.org/format/2502.17097">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"> Rotatable Antenna Enabled Wireless Communication System with Visual Recognition: A Prototype Implementation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Dai%2C+L">Liang Dai</a>, <a href="/search/eess?searchtype=author&query=Zheng%2C+B">Beixiong Zheng</a>, <a href="/search/eess?searchtype=author&query=Tan%2C+Y">Yanhua Tan</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+F">Fangjiong Chen</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="2502.17097v2-abstract-short" style="display: inline;"> Rotatable antenna (RA) is an emerging technology that has great potential to exploit additional spatial degrees of freedom (DoFs) by flexibly altering the three-dimensional (3D) orientation/boresight of each antenna. In this demonstration, we present a prototype of the RA-enabled wireless communication system with a visual recognition module to evaluate the performance gains provided by the RA in… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17097v2-abstract-full').style.display = 'inline'; document.getElementById('2502.17097v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17097v2-abstract-full" style="display: none;"> Rotatable antenna (RA) is an emerging technology that has great potential to exploit additional spatial degrees of freedom (DoFs) by flexibly altering the three-dimensional (3D) orientation/boresight of each antenna. In this demonstration, we present a prototype of the RA-enabled wireless communication system with a visual recognition module to evaluate the performance gains provided by the RA in practical environments. In particular, a mechanically-driven RA is developed by integrating a digital servo motor, a directional antenna, and a microcontroller, which enables the dynamic adjustment of the RA orientation. Moreover, the orientation adjustment of the RA is guided by the user's direction information provided by the visual recognition module, thereby significantly enhancing system response speed and self-orientation accuracy. The experimental results demonstrate that the RA-enabled communication system achieves significant improvement in communication coverage performance compared to the conventional fixed antenna system. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17097v2-abstract-full').style.display = 'none'; document.getElementById('2502.17097v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.17085">arXiv:2502.17085</a> <span> [<a href="https://arxiv.org/pdf/2502.17085">pdf</a>, <a href="https://arxiv.org/format/2502.17085">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Pleno-Generation: A Scalable Generative Face Video Compression Framework with Bandwidth Intelligence </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+B">Bolin Chen</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+H">Hanwei Zhu</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+S">Shanzhi Yin</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/eess?searchtype=author&query=Liao%2C+R">Ru-Ling Liao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shiqi Wang</a>, <a href="/search/eess?searchtype=author&query=Ye%2C+Y">Yan Ye</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.17085v1-abstract-short" style="display: inline;"> Generative model based compact video compression is typically operated within a relative narrow range of bitrates, and often with an emphasis on ultra-low rate applications. There has been an increasing consensus in the video communication industry that full bitrate coverage should be enabled by generative coding. However, this is an extremely difficult task, largely because generation and compres… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17085v1-abstract-full').style.display = 'inline'; document.getElementById('2502.17085v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.17085v1-abstract-full" style="display: none;"> Generative model based compact video compression is typically operated within a relative narrow range of bitrates, and often with an emphasis on ultra-low rate applications. There has been an increasing consensus in the video communication industry that full bitrate coverage should be enabled by generative coding. However, this is an extremely difficult task, largely because generation and compression, although related, have distinct goals and trade-offs. The proposed Pleno-Generation (PGen) framework distinguishes itself through its exceptional capabilities in ensuring the robustness of video coding by utilizing a wider range of bandwidth for generation via bandwidth intelligence. In particular, we initiate our research of PGen with face video coding, and PGen offers a paradigm shift that prioritizes high-fidelity reconstruction over pursuing compact bitstream. The novel PGen framework leverages scalable representation and layered reconstruction for Generative Face Video Compression (GFVC), in an attempt to imbue the bitstream with intelligence in different granularity. Experimental results illustrate that the proposed PGen framework can facilitate existing GFVC algorithms to better deliver high-fidelity and faithful face videos. In addition, the proposed framework can allow a greater space of flexibility for coding applications and show superior RD performance with a much wider bitrate range in terms of various quality evaluations. Moreover, in comparison with the latest Versatile Video Coding (VVC) codec, the proposed scheme achieves competitive Bj酶ntegaard-delta-rate savings for perceptual-level evaluations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.17085v1-abstract-full').style.display = 'none'; document.getElementById('2502.17085v1-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 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.11378">arXiv:2502.11378</a> <span> [<a href="https://arxiv.org/pdf/2502.11378">pdf</a>, <a href="https://arxiv.org/format/2502.11378">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"> Numerical Differentiation-based Electrophysiology-Aware Adaptive ResNet for Inverse ECG Modeling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingzhen Zhu</a>, <a href="/search/eess?searchtype=author&query=Bilchick%2C+K">Kenneth Bilchick</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+J">Jianxin Xie</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.11378v1-abstract-short" style="display: inline;"> Electrocardiographic imaging aims to noninvasively reconstruct the electrical dynamic patterns on the heart surface from body-surface ECG measurements, aiding the mechanistic study of cardiac function. At the core of ECGI lies the inverse ECG problem, a mathematically ill-conditioned challenge where small body measurement errors or noise can lead to significant inaccuracies in the reconstructed he… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11378v1-abstract-full').style.display = 'inline'; document.getElementById('2502.11378v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.11378v1-abstract-full" style="display: none;"> Electrocardiographic imaging aims to noninvasively reconstruct the electrical dynamic patterns on the heart surface from body-surface ECG measurements, aiding the mechanistic study of cardiac function. At the core of ECGI lies the inverse ECG problem, a mathematically ill-conditioned challenge where small body measurement errors or noise can lead to significant inaccuracies in the reconstructed heart-surface potentials. %Leveraging a well-developed electrophysiological (EP) model, our previous study developed an EP-informed deep learning framework, demonstrating promising effectiveness in improving cardiac map predictions. To improve the accuracy of ECGI and ensure that cardiac predictions adhere to established physical principles, recent advances have incorporated well-established electrophysiology (EP) laws into their model formulations. However, traditional EP-informed models encounter significant challenges, including overfitting to EP constraints, limitations in network scalability, and suboptimal initialization. These issues compromise prediction accuracy and stability, hindering their effectiveness in practical applications. This highlights the need for an advanced data analytic and predictive tool to achieve reliable cardiac electrodynamic restoration. Here, we present a Numerical Differentiation-based Electrophysiology-Aware Adaptive Residual neural Network (EAND-ARN) for robust inverse ECG modeling. Our method employs numerical differentiation to compute the spatiotemporal derivative, enabling EP constraints to be applied across a local spatiotemporal region, thereby strengthening the overall EP enforcement. Additionally, we design an adaptive residual network to improve gradient flow, enhancing predictive accuracy and mitigating issues with poor initialization. Experimental results show that EAND-ARN significantly outperforms existing methods in current practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.11378v1-abstract-full').style.display = 'none'; document.getElementById('2502.11378v1-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, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.15368">arXiv:2501.15368</a> <span> [<a href="https://arxiv.org/pdf/2501.15368">pdf</a>, <a href="https://arxiv.org/format/2501.15368">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Baichuan-Omni-1.5 Technical Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yadong Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+J">Jun Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+T">Tao Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+T">Tao Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+S">Song Chen</a>, <a href="/search/eess?searchtype=author&query=Li%2C+T">Tianpeng Li</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zehuan Li</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Lijun Liu</a>, <a href="/search/eess?searchtype=author&query=Ming%2C+L">Lingfeng Ming</a>, <a href="/search/eess?searchtype=author&query=Dong%2C+G">Guosheng Dong</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+D">Da Pan</a>, <a href="/search/eess?searchtype=author&query=Li%2C+C">Chong Li</a>, <a href="/search/eess?searchtype=author&query=Fang%2C+Y">Yuanbo Fang</a>, <a href="/search/eess?searchtype=author&query=Kuang%2C+D">Dongdong Kuang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+M">Mingrui Wang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+C">Chenglin Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Youwei Zhang</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+H">Hongyu Guo</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+F">Fengyu Zhang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yuran Wang</a>, <a href="/search/eess?searchtype=author&query=Ding%2C+B">Bowen Ding</a>, <a href="/search/eess?searchtype=author&query=Song%2C+W">Wei Song</a>, <a href="/search/eess?searchtype=author&query=Li%2C+X">Xu Li</a>, <a href="/search/eess?searchtype=author&query=Huo%2C+Y">Yuqi Huo</a>, <a href="/search/eess?searchtype=author&query=Liang%2C+Z">Zheng Liang</a> , et al. (68 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.15368v1-abstract-short" style="display: inline;"> We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pip… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15368v1-abstract-full').style.display = 'inline'; document.getElementById('2501.15368v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.15368v1-abstract-full" style="display: none;"> We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three key aspects. First, we establish a comprehensive data cleaning and synthesis pipeline for multimodal data, obtaining about 500B high-quality data (text, audio, and vision). Second, an audio-tokenizer (Baichuan-Audio-Tokenizer) has been designed to capture both semantic and acoustic information from audio, enabling seamless integration and enhanced compatibility with MLLM. Lastly, we designed a multi-stage training strategy that progressively integrates multimodal alignment and multitask fine-tuning, ensuring effective synergy across all modalities. Baichuan-Omni-1.5 leads contemporary models (including GPT4o-mini and MiniCPM-o 2.6) in terms of comprehensive omni-modal capabilities. Notably, it achieves results comparable to leading models such as Qwen2-VL-72B across various multimodal medical benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.15368v1-abstract-full').style.display = 'none'; document.getElementById('2501.15368v1-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 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.07989">arXiv:2501.07989</a> <span> [<a href="https://arxiv.org/pdf/2501.07989">pdf</a>, <a href="https://arxiv.org/ps/2501.07989">ps</a>, <a href="https://arxiv.org/format/2501.07989">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"> Movable Antenna Enhanced DF and AF Relaying Systems: Performance Analysis and Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+N">Nianzu Li</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+P">Peiran Wu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.07989v1-abstract-short" style="display: inline;"> Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between tw… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07989v1-abstract-full').style.display = 'inline'; document.getElementById('2501.07989v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.07989v1-abstract-full" style="display: none;"> Movable antenna (MA) has been deemed as a promising technology to flexibly reconfigure wireless channels by adjusting the antenna positions in a given local region. In this paper, we investigate the application of the MA technology in both decode-and-forward (DF) and amplify-and-forward (AF) relaying systems, where a relay is equipped with multiple MAs to assist in the data transmission between two single-antenna nodes. For the DF relaying system, our objective is to maximize the achievable rate at the destination by jointly optimizing the positions of the MAs in two stages for receiving signals from the source and transmitting signals to the destination, respectively. To drive essential insights, we first derive a closed-form upper bound on the maximum achievable rate of the DF relaying system. Then, a low-complexity algorithm based on projected gradient ascent (PGA) and alternating optimization (AO) is proposed to solve the antenna position optimization problem. For the AF relaying system, our objective is to maximize the achievable rate by jointly optimizing the two-stage MA positions as well as the AF beamforming matrix at the relay, which results in a more challenging optimization problem due to the intricate coupling variables. To tackle this challenge, we first reveal the hidden separability among the antenna position optimization in the two stages and the beamforming optimization. Based on such separability, we derive a closed-form upper bound on the maximum achievable rate of the AF relaying system and propose a low-complexity algorithm to obtain a high-quality suboptimal solution to the considered problem. Simulation results validate the efficacy of our theoretical analysis and demonstrate the superiority of the MA-enhanced relaying systems to the conventional relaying systems with fixed-position antennas (FPAs) and other benchmark schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07989v1-abstract-full').style.display = 'none'; document.getElementById('2501.07989v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.07318">arXiv:2501.07318</a> <span> [<a href="https://arxiv.org/pdf/2501.07318">pdf</a>, <a href="https://arxiv.org/ps/2501.07318">ps</a>, <a href="https://arxiv.org/format/2501.07318">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"> Movable Antenna Enhanced Integrated Sensing and Communication Via Antenna Position Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ma%2C+W">Wenyan Ma</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2501.07318v2-abstract-short" style="display: inline;"> In this paper, we propose an integrated sensing and communication (ISAC) system aided by the movable-antenna (MA) array, which can improve the communication and sensing performance via flexible antenna movement over conventional fixed-position antenna (FPA) array. First, we consider the downlink multiuser communication, where each user is randomly distributed within a given three-dimensional zone… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07318v2-abstract-full').style.display = 'inline'; document.getElementById('2501.07318v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.07318v2-abstract-full" style="display: none;"> In this paper, we propose an integrated sensing and communication (ISAC) system aided by the movable-antenna (MA) array, which can improve the communication and sensing performance via flexible antenna movement over conventional fixed-position antenna (FPA) array. First, we consider the downlink multiuser communication, where each user is randomly distributed within a given three-dimensional zone with local movement. To reduce the overhead of frequent antenna movement, the antenna position vector (APV) is designed based on users' statistical channel state information (CSI), so that the antennas only need to be moved in a large timescale. Then, for target sensing, the Cramer-Rao bounds (CRBs) of the estimation mean square error for different spatial angles of arrival (AoAs) are derived as functions of MAs' positions. Based on the above, we formulate an optimization problem to maximize the expected minimum achievable rate among all communication users, with given constraints on the maximum acceptable CRB thresholds for target sensing. An alternating optimization algorithm is proposed to iteratively optimize one of the horizontal and vertical APVs of the MA array with the other being fixed. Numerical results demonstrate that our proposed MA arrays can significantly enlarge the trade-off region between communication and sensing performance compared to conventional FPA arrays with different inter-antenna spacing. It is also revealed that the steering vectors of the designed MA arrays exhibit low correlation in the angular domain, thus effectively reducing channel correlation among communication users to enhance their achievable rates, while alleviating ambiguity in target angle estimation to achieve improved sensing accuracy. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.07318v2-abstract-full').style.display = 'none'; document.getElementById('2501.07318v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 14 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.17088">arXiv:2412.17088</a> <span> [<a href="https://arxiv.org/pdf/2412.17088">pdf</a>, <a href="https://arxiv.org/format/2412.17088">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"> 6DMA-Aided Hybrid Beamforming with Joint Antenna Position and Orientation Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yichi Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yuchen Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+S">Sa Xiao</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+W">Wanbin Tang</a>, <a href="/search/eess?searchtype=author&query=Eldar%2C+Y+C">Yonina C. Eldar</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="2412.17088v1-abstract-short" style="display: inline;"> This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system. In this system, each sub-array is connected to a dedicated radio frequency chain and collectively moves and rotates as a unit within specific local regions. The movement and rotation capabilities of 6DMAs enhance design flexibility, facilitating the capture of spatial variations for imp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.17088v1-abstract-full').style.display = 'inline'; document.getElementById('2412.17088v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.17088v1-abstract-full" style="display: none;"> This paper studies a sub-connected six-dimensional movable antenna (6DMA)-aided multi-user communication system. In this system, each sub-array is connected to a dedicated radio frequency chain and collectively moves and rotates as a unit within specific local regions. The movement and rotation capabilities of 6DMAs enhance design flexibility, facilitating the capture of spatial variations for improved communication performance. To fully characterize the effect of antenna position and orientation on wireless channels between the base station (BS) and users, we develop a field-response-based 6DMA channel model to account for the antenna radiation pattern and polarization. We then maximize the sum rate of multiple users, by jointly optimizing the digital and unit-modulus analog beamformers given the transmit power budget as well as the positions and orientations of sub-arrays within given movable and rotatable ranges at the BS. Due to the highly coupled variables, the formulated optimization problem is non-convex and thus challenging to solve. We develop a fractional programming-aided alternating optimization framework that integrates the Lagrange multiplier method, manifold optimization, and gradient descent to solve the problem. Numerical results demonstrate that the proposed 6DMA-aided sub-connected structure achieves a substantial sum-rate improvement over various benchmark schemes with less flexibility in antenna movement and can even outperform fully-digital beamforming systems that employ antenna position or orientation adjustments only. The results also highlight the necessity of considering antenna polarization for optimally adjusting antenna orientation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.17088v1-abstract-full').style.display = 'none'; document.getElementById('2412.17088v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">The conference version of this paper has been accepted for Globecom 2024 Workshop</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.12531">arXiv:2412.12531</a> <span> [<a href="https://arxiv.org/pdf/2412.12531">pdf</a>, <a href="https://arxiv.org/ps/2412.12531">ps</a>, <a href="https://arxiv.org/format/2412.12531">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"> Movable Antenna Aided NOMA: Joint Antenna Positioning, Precoding, and Decoding Design </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Z">Zhe Li</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=da+Costa%2C+D+B">Daniel Benevides da Costa</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+X">Xiang-Gen Xia</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="2412.12531v1-abstract-short" style="display: inline;"> This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable rate among all the users by jointly optimizing the MA positioning of each user, t… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.12531v1-abstract-full').style.display = 'inline'; document.getElementById('2412.12531v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.12531v1-abstract-full" style="display: none;"> This paper investigates movable antenna (MA) aided non-orthogonal multiple access (NOMA) for multi-user downlink communication, where the base station (BS) is equipped with a fixed-position antenna (FPA) array to serve multiple MA-enabled users. An optimization problem is formulated to maximize the minimum achievable rate among all the users by jointly optimizing the MA positioning of each user, the precoding matrix at the BS, and the successive interference cancellation (SIC) decoding indicator matrix at the users, subject to a set of constraints including the limited movement area of the MAs, the maximum transmit power of the BS, and the SIC decoding condition. To solve this non-convex problem, we propose a two-loop iterative optimization algorithm that combines the hippopotamus optimization (HO) method with the alternating optimization (AO) method to obtain a suboptimal solution efficiently. Specifically, in the inner loop, the complex-valued precoding matrix and the binary decoding indicator matrix are optimized alternatively by the successive convex approximation (SCA) technique with customized greedy search to maximize the minimum achievable rate for the given positions of the MAs. In the outer loop, each user's antenna position is updated using the HO algorithm, following a novel nature-inspired intelligent optimization framework. Simulation results show that the proposed algorithms can effectively avoid local optimum for highly coupled variables and significantly improve the rate performance of the NOMA system compared to the conventional FPA system as well as other benchmark schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.12531v1-abstract-full').style.display = 'none'; document.getElementById('2412.12531v1-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.10736">arXiv:2412.10736</a> <span> [<a href="https://arxiv.org/pdf/2412.10736">pdf</a>, <a href="https://arxiv.org/format/2412.10736">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"> 6D Movable Antenna Enhanced Multi-Access Point Coordination via Position and Orientation Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pi%2C+X">Xiangyu Pi</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Mao%2C+H">Haobin Mao</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+X">Xiang-Gen Xia</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="2412.10736v1-abstract-short" style="display: inline;"> The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10736v1-abstract-full').style.display = 'inline'; document.getElementById('2412.10736v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.10736v1-abstract-full" style="display: none;"> The effective utilization of unlicensed spectrum is regarded as an important direction to enable the massive access and broad coverage for next-generation wireless local area network (WLAN). Due to the crowded spectrum occupancy and dense user terminals (UTs), the conventional fixed antenna (FA)-based access points (APs) face huge challenges in realizing massive access and interference cancellation. To address this issue, in this paper we develop a six-dimensional movable antenna (6DMA) enhanced multi-AP coordination system for coverage enhancement and interference mitigation. First, we model the wireless channels between the APs and UTs to characterize their variation with respect to 6DMA movement, in terms of both the three-dimensional (3D) position and 3D orientation of each distributed AP's antenna. Then, an optimization problem is formulated to maximize the weighted sum rate of multiple UTs for their uplink transmissions by jointly optimizing the antenna position vector (APV), the antenna orientation matrix (AOM), and the receive combining matrix over all coordinated APs, subject to the constraints on local antenna movement regions. To solve this challenging non-convex optimization problem, we first transform it into a more tractable Lagrangian dual problem. Then, an alternating optimization (AO)-based algorithm is developed by iteratively optimizing the APV and AOM, which are designed by applying the successive convex approximation (SCA) technique and Riemannian manifold optimization-based algorithm, respectively. Simulation results show that the proposed 6DMA-enhanced multi-AP coordination system can significantly enhance network capacity, and both of the online and offline 6DMA schemes can attain considerable performance improvement compared to the conventional FA-based schemes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.10736v1-abstract-full').style.display = 'none'; document.getElementById('2412.10736v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 9 figures, submitted to an IEEE journal for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.14798">arXiv:2411.14798</a> <span> [<a href="https://arxiv.org/pdf/2411.14798">pdf</a>, <a href="https://arxiv.org/format/2411.14798">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Facial Features Matter: a Dynamic Watermark based Proactive Deepfake Detection Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Lan%2C+S">Shulin Lan</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+K">Kanlin Liu</a>, <a href="/search/eess?searchtype=author&query=Zhao%2C+Y">Yazhou Zhao</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+C">Chen Yang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yingchao Wang</a>, <a href="/search/eess?searchtype=author&query=Yao%2C+X">Xingshan Yao</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Liehuang Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.14798v1-abstract-short" style="display: inline;"> Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14798v1-abstract-full').style.display = 'inline'; document.getElementById('2411.14798v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.14798v1-abstract-full" style="display: none;"> Current passive deepfake face-swapping detection methods encounter significance bottlenecks in model generalization capabilities. Meanwhile, proactive detection methods often use fixed watermarks which lack a close relationship with the content they protect and are vulnerable to security risks. Dynamic watermarks based on facial features offer a promising solution, as these features provide unique identifiers. Therefore, this paper proposes a Facial Feature-based Proactive deepfake detection method (FaceProtect), which utilizes changes in facial characteristics during deepfake manipulation as a novel detection mechanism. We introduce a GAN-based One-way Dynamic Watermark Generating Mechanism (GODWGM) that uses 128-dimensional facial feature vectors as inputs. This method creates irreversible mappings from facial features to watermarks, enhancing protection against various reverse inference attacks. Additionally, we propose a Watermark-based Verification Strategy (WVS) that combines steganography with GODWGM, allowing simultaneous transmission of the benchmark watermark representing facial features within the image. Experimental results demonstrate that our proposed method maintains exceptional detection performance and exhibits high practicality on images altered by various deepfake techniques. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.14798v1-abstract-full').style.display = 'none'; document.getElementById('2411.14798v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.13983">arXiv:2411.13983</a> <span> [<a href="https://arxiv.org/pdf/2411.13983">pdf</a>, <a href="https://arxiv.org/format/2411.13983">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Kim%2C+H">Hansung Kim</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+E+L">Edward L. Zhu</a>, <a href="/search/eess?searchtype=author&query=Lim%2C+C+S">Chang Seok Lim</a>, <a href="/search/eess?searchtype=author&query=Borrelli%2C+F">Francesco Borrelli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.13983v3-abstract-short" style="display: inline;"> We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach appl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13983v3-abstract-full').style.display = 'inline'; document.getElementById('2411.13983v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.13983v3-abstract-full" style="display: none;"> We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach applies to competitive and cooperative multi-agent motion planning problems which we formulate as constrained dynamic games. Given a constrained dynamic game, we randomly sample initial conditions and solve for the generalized Nash equilibrium (GNE) to generate a dataset of GNE solutions, computing the reward outcome of each game-theoretic interaction from the GNE. The data is used to train a simple neural network to predict the reward outcome, which we use as the terminal cost-to-go function in an MPC scheme. We showcase emerging competitive and coordinated behaviors using IGT-MPC in scenarios such as two-vehicle head-to-head racing and un-signalized intersection navigation. IGT-MPC offers a novel method integrating machine learning and game-theoretic reasoning into model-based decentralized multi-agent motion planning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.13983v3-abstract-full').style.display = 'none'; document.getElementById('2411.13983v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted Proceeding at 2025 Learning for Dynamics and Control Conference (L4DC)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.12791">arXiv:2411.12791</a> <span> [<a href="https://arxiv.org/pdf/2411.12791">pdf</a>, <a href="https://arxiv.org/format/2411.12791">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> </div> <p class="title is-5 mathjax"> Mitigating Perception Bias: A Training-Free Approach to Enhance LMM for Image Quality Assessment </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Pan%2C+S">Siyi Pan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+B">Baoliang Chen</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+D">Danni Huang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+H">Hanwei Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Sui%2C+X">Xiangjie Sui</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shiqi Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.12791v1-abstract-short" style="display: inline;"> Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quality-insensitive perception bias inevitabl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12791v1-abstract-full').style.display = 'inline'; document.getElementById('2411.12791v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.12791v1-abstract-full" style="display: none;"> Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quality-insensitive perception bias inevitably leads to a heavy reliance on image semantics when those LMMs are forced for quality rating. In this paper, instead of retraining or tuning an LMM costly, we propose a training-free debiasing framework, in which the image quality prediction is rectified by mitigating the bias caused by image semantics. Specifically, we first explore several semantic-preserving distortions that can significantly degrade image quality while maintaining identifiable semantics. By applying these specific distortions to the query or test images, we ensure that the degraded images are recognized as poor quality while their semantics remain. During quality inference, both a query image and its corresponding degraded version are fed to the LMM along with a prompt indicating that the query image quality should be inferred under the condition that the degraded one is deemed poor quality.This prior condition effectively aligns the LMM's quality perception, as all degraded images are consistently rated as poor quality, regardless of their semantic difference.Finally, the quality scores of the query image inferred under different prior conditions (degraded versions) are aggregated using a conditional probability model. Extensive experiments on various IQA datasets show that our debiasing framework could consistently enhance the LMM performance and the code will be publicly available. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.12791v1-abstract-full').style.display = 'none'; document.getElementById('2411.12791v1-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.00374">arXiv:2411.00374</a> <span> [<a href="https://arxiv.org/pdf/2411.00374">pdf</a>, <a href="https://arxiv.org/ps/2411.00374">ps</a>, <a href="https://arxiv.org/format/2411.00374">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"> RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Sun%2C+H">He Sun</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</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="2411.00374v3-abstract-short" style="display: inline;"> The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00374v3-abstract-full').style.display = 'inline'; document.getElementById('2411.00374v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.00374v3-abstract-full" style="display: none;"> The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing powermeasurement-based IRS reflection designs in wideband channels. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.00374v3-abstract-full').style.display = 'none'; document.getElementById('2411.00374v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 1 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.21799">arXiv:2410.21799</a> <span> [<a href="https://arxiv.org/pdf/2410.21799">pdf</a>, <a href="https://arxiv.org/ps/2410.21799">ps</a>, <a href="https://arxiv.org/format/2410.21799">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">stat.ML</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"> Exponentially Consistent Statistical Classification of Continuous Sequences with Distribution Uncertainty </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lina Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+L">Lin 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="2410.21799v1-abstract-short" style="display: inline;"> In multiple classification, one aims to determine whether a testing sequence is generated from the same distribution as one of the M training sequences or not. Unlike most of existing studies that focus on discrete-valued sequences with perfect distribution match, we study multiple classification for continuous sequences with distribution uncertainty, where the generating distributions of the test… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21799v1-abstract-full').style.display = 'inline'; document.getElementById('2410.21799v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21799v1-abstract-full" style="display: none;"> In multiple classification, one aims to determine whether a testing sequence is generated from the same distribution as one of the M training sequences or not. Unlike most of existing studies that focus on discrete-valued sequences with perfect distribution match, we study multiple classification for continuous sequences with distribution uncertainty, where the generating distributions of the testing and training sequences deviate even under the true hypothesis. In particular, we propose distribution free tests and prove that the error probabilities of our tests decay exponentially fast for three different test designs: fixed-length, sequential, and two-phase tests. We first consider the simple case without the null hypothesis, where the testing sequence is known to be generated from a distribution close to the generating distribution of one of the training sequences. Subsequently, we generalize our results to a more general case with the null hypothesis by allowing the testing sequence to be generated from a distribution that is vastly different from the generating distributions of all training sequences. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21799v1-abstract-full').style.display = 'none'; document.getElementById('2410.21799v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">arXiv admin note: substantial text overlap with arXiv:2405.01161</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.21240">arXiv:2410.21240</a> <span> [<a href="https://arxiv.org/pdf/2410.21240">pdf</a>, <a href="https://arxiv.org/format/2410.21240">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"> Quantum Reinforcement Learning-Based Two-Stage Unit Commitment Framework for Enhanced Power Systems Robustness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wei%2C+X">Xiang Wei</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Ziqing Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linghua Zhu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+Z">Ze Hu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xian Zhang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+G">Guibin Wang</a>, <a href="/search/eess?searchtype=author&query=Bu%2C+S">Siqi Bu</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+K+W">Ka Wing Chan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.21240v3-abstract-short" style="display: inline;"> Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorporate widely adopted virtual power plants (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21240v3-abstract-full').style.display = 'inline'; document.getElementById('2410.21240v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.21240v3-abstract-full" style="display: none;"> Unit commitment (UC) optimizes the start-up and shutdown schedules of generating units to meet load demand while minimizing costs. However, the increasing integration of renewable energy introduces uncertainties for real-time scheduling. Existing solutions face limitations both in modeling and algorithmic design. At the modeling level, they fail to incorporate widely adopted virtual power plants (VPPs) as flexibility resources, missing the opportunity to proactively mitigate potential real-time imbalances or ramping constraints through foresight-seeing decision-making. At the algorithmic level, existing probabilistic optimization, multi-stage approaches, and machine learning, face challenges in computational complexity and adaptability. To address these challenges, this study proposes a novel two-stage UC framework that incorporates foresight-seeing sequential decision-making in both day-ahead and real-time scheduling, leveraging VPPs as flexibility resources to proactively reserve capacity and ramping flexibility for upcoming renewable energy uncertainties over several hours. In particular, we develop quantum reinforcement learning (QRL) algorithms that integrate the foresight-seeing sequential decision-making and scalable computation advantages of deep reinforcement learning (DRL) with the parallel and high-efficiency search capabilities of quantum computing. Experimental results demonstrate that the proposed QRL-based approach outperforms in computational efficiency, real-time responsiveness, and solution quality. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.21240v3-abstract-full').style.display = 'none'; document.getElementById('2410.21240v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 22 March, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 28 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20275">arXiv:2410.20275</a> <span> [<a href="https://arxiv.org/pdf/2410.20275">pdf</a>, <a href="https://arxiv.org/format/2410.20275">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"> Advancing Hybrid Quantum Neural Network for Alternative Current Optimal Power Flow </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Hu%2C+Z">Ze Hu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Ziqing Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linghua Zhu</a>, <a href="/search/eess?searchtype=author&query=Wei%2C+X">Xiang Wei</a>, <a href="/search/eess?searchtype=author&query=Bu%2C+S">Siqi Bu</a>, <a href="/search/eess?searchtype=author&query=Chan%2C+K+W">Ka Wing Chan</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.20275v4-abstract-short" style="display: inline;"> Alternative Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and real-time operation but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid classical-quantum deep learning framework for AC-OPF problem, integrating parameterized quantum circuits (PQCs) for feature extraction with cl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20275v4-abstract-full').style.display = 'inline'; document.getElementById('2410.20275v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20275v4-abstract-full" style="display: none;"> Alternative Current Optimal Power Flow (AC-OPF) is essential for efficient power system planning and real-time operation but remains an NP-hard and non-convex optimization problem with significant computational challenges. This paper proposes a novel hybrid classical-quantum deep learning framework for AC-OPF problem, integrating parameterized quantum circuits (PQCs) for feature extraction with classical deep learning for data encoding and decoding. The proposed framework integrates two types of residual connection structures to mitigate the ``barren plateau" problem in quantum circuits, enhancing training stability and convergence. Furthermore, a physics-informed neural network (PINN) module is incorporated to guarantee tolerable constraint violation, improving the physical consistency and reliability of AC-OPF solutions. Experimental evaluations on multiple IEEE test systems demonstrate that the proposed approach achieves superior accuracy, generalization, and robustness to quantum noise while requiring minimal quantum resources. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20275v4-abstract-full').style.display = 'none'; document.getElementById('2410.20275v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.20042">arXiv:2410.20042</a> <span> [<a href="https://arxiv.org/pdf/2410.20042">pdf</a>, <a href="https://arxiv.org/format/2410.20042">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> </div> <p class="title is-5 mathjax"> Multi-IRS Enhanced Wireless Coverage: Deployment Optimization Based on Large-Scale Channel Knowledge </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Fu%2C+M">Min Fu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2410.20042v1-abstract-short" style="display: inline;"> In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grids of identical size. Then, we derive the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20042v1-abstract-full').style.display = 'inline'; document.getElementById('2410.20042v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.20042v1-abstract-full" style="display: none;"> In this paper, we study the intelligent reflecting surface (IRS) deployment problem where a number of IRSs are optimally placed in a target area to improve its signal coverage with the serving base station (BS). To achieve this, we assume that there is a given set of candidate sites in the target area for deploying IRSs and divide the area into multiple grids of identical size. Then, we derive the average channel power gains from the BS to IRS in each candidate site and from this IRS to any grid in the target area in terms of IRS deployment parameters, including its size, position, height, and orientation. Thus, we are able to approximate the average cascaded channel power gain from the BS to each grid via any IRS, assuming an effective IRS reflection gain based on the large-scale channel knowledge only. Next, we formulate a multi-IRS deployment optimization problem to minimize the total deployment cost by selecting a subset of candidate sites for deploying IRSs and jointly optimizing their heights, orientations, and numbers of reflecting elements while satisfying a given coverage rate performance requirement over all grids in the target area. To solve this challenging combinatorial optimization problem, we first reformulate it as an integer linear programming problem and solve it optimally using the branch-and-bound (BB) algorithm. In addition, we propose an efficient successive refinement algorithm to further reduce computational complexity. Simulation results demonstrate that the proposed lower-complexity successive refinement algorithm achieves near-optimal performance but with significantly reduced running time compared to the proposed optimal BB algorithm, as well as superior performance-cost trade-off than other baseline IRS deployment strategies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.20042v1-abstract-full').style.display = 'none'; document.getElementById('2410.20042v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.19765">arXiv:2410.19765</a> <span> [<a href="https://arxiv.org/pdf/2410.19765">pdf</a>, <a href="https://arxiv.org/format/2410.19765">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1007/978-3-031-72117-5_2">10.1007/978-3-031-72117-5_2 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A New Perspective to Boost Performance Fairness for Medical Federated Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+Y">Yunlu Yan</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lei Zhu</a>, <a href="/search/eess?searchtype=author&query=Li%2C+Y">Yuexiang Li</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+X">Xinxing Xu</a>, <a href="/search/eess?searchtype=author&query=Goh%2C+R+S+M">Rick Siow Mong Goh</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yong Liu</a>, <a href="/search/eess?searchtype=author&query=Khan%2C+S">Salman Khan</a>, <a href="/search/eess?searchtype=author&query=Feng%2C+C">Chun-Mei Feng</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.19765v1-abstract-short" style="display: inline;"> Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the specific characteristics of medical FL applications, i.e., domain shift among the datasets from different hospitals. In this work, we propose Fed-LWR to improve performance fairness from the perspective of feature shift,… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19765v1-abstract-full').style.display = 'inline'; document.getElementById('2410.19765v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.19765v1-abstract-full" style="display: none;"> Improving the fairness of federated learning (FL) benefits healthy and sustainable collaboration, especially for medical applications. However, existing fair FL methods ignore the specific characteristics of medical FL applications, i.e., domain shift among the datasets from different hospitals. In this work, we propose Fed-LWR to improve performance fairness from the perspective of feature shift, a key issue influencing the performance of medical FL systems caused by domain shift. Specifically, we dynamically perceive the bias of the global model across all hospitals by estimating the layer-wise difference in feature representations between local and global models. To minimize global divergence, we assign higher weights to hospitals with larger differences. The estimated client weights help us to re-aggregate the local models per layer to obtain a fairer global model. We evaluate our method on two widely used federated medical image segmentation benchmarks. The results demonstrate that our method achieves better and fairer performance compared with several state-of-the-art fair FL methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19765v1-abstract-full').style.display = 'none'; document.getElementById('2410.19765v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 2 Figures</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Conference on Medical Image Computing and Computer-Assisted Intervention 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.19452">arXiv:2410.19452</a> <span> [<a href="https://arxiv.org/pdf/2410.19452">pdf</a>, <a href="https://arxiv.org/format/2410.19452">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Gong%2C+Z">Zixuan Gong</a>, <a href="/search/eess?searchtype=author&query=Bao%2C+G">Guangyin Bao</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Q">Qi Zhang</a>, <a href="/search/eess?searchtype=author&query=Wan%2C+Z">Zhongwei Wan</a>, <a href="/search/eess?searchtype=author&query=Miao%2C+D">Duoqian Miao</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shoujin Wang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lei Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+C">Changwei Wang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+R">Rongtao Xu</a>, <a href="/search/eess?searchtype=author&query=Hu%2C+L">Liang Hu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+K">Ke Liu</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yu 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="2410.19452v3-abstract-short" style="display: inline;"> Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of continuous visual experiences is formidably challenging. We contend that the key to addressing these chal… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19452v3-abstract-full').style.display = 'inline'; document.getElementById('2410.19452v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.19452v3-abstract-full" style="display: none;"> Reconstruction of static visual stimuli from non-invasion brain activity fMRI achieves great success, owning to advanced deep learning models such as CLIP and Stable Diffusion. However, the research on fMRI-to-video reconstruction remains limited since decoding the spatiotemporal perception of continuous visual experiences is formidably challenging. We contend that the key to addressing these challenges lies in accurately decoding both high-level semantics and low-level perception flows, as perceived by the brain in response to video stimuli. To the end, we propose NeuroClips, an innovative framework to decode high-fidelity and smooth video from fMRI. NeuroClips utilizes a semantics reconstructor to reconstruct video keyframes, guiding semantic accuracy and consistency, and employs a perception reconstructor to capture low-level perceptual details, ensuring video smoothness. During inference, it adopts a pre-trained T2V diffusion model injected with both keyframes and low-level perception flows for video reconstruction. Evaluated on a publicly available fMRI-video dataset, NeuroClips achieves smooth high-fidelity video reconstruction of up to 6s at 8FPS, gaining significant improvements over state-of-the-art models in various metrics, e.g., a 128% improvement in SSIM and an 81% improvement in spatiotemporal metrics. Our project is available at https://github.com/gongzix/NeuroClips. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.19452v3-abstract-full').style.display = 'none'; document.getElementById('2410.19452v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 25 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">NeurIPS 2024 Oral</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16626">arXiv:2410.16626</a> <span> [<a href="https://arxiv.org/pdf/2410.16626">pdf</a>, <a href="https://arxiv.org/ps/2410.16626">ps</a>, <a href="https://arxiv.org/format/2410.16626">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"> Codebook Design and Performance Analysis for Wideband Beamforming in Terahertz Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhi Chen</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="2410.16626v1-abstract-short" style="display: inline;"> The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the ``beam squi… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16626v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16626v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16626v1-abstract-full" style="display: none;"> The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the ``beam squint" effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16626v1-abstract-full').style.display = 'none'; document.getElementById('2410.16626v1-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 pages, 8 figures. Accepted for publication by IEEE TWC</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14965">arXiv:2410.14965</a> <span> [<a href="https://arxiv.org/pdf/2410.14965">pdf</a>, <a href="https://arxiv.org/format/2410.14965">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"> Non-Invasive to Invasive: Enhancing FFA Synthesis from CFP with a Benchmark Dataset and a Novel Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+H">Hongqiu Wang</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+Z">Zhaohu Xing</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+W">Weitong Wu</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yijun Yang</a>, <a href="/search/eess?searchtype=author&query=Tang%2C+Q">Qingqing Tang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Meixia Zhang</a>, <a href="/search/eess?searchtype=author&query=Xu%2C+Y">Yanwu Xu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lei Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14965v1-abstract-short" style="display: inline;"> Fundus imaging is a pivotal tool in ophthalmology, and different imaging modalities are characterized by their specific advantages. For example, Fundus Fluorescein Angiography (FFA) uniquely provides detailed insights into retinal vascular dynamics and pathology, surpassing Color Fundus Photographs (CFP) in detecting microvascular abnormalities and perfusion status. However, the conventional invas… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14965v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14965v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14965v1-abstract-full" style="display: none;"> Fundus imaging is a pivotal tool in ophthalmology, and different imaging modalities are characterized by their specific advantages. For example, Fundus Fluorescein Angiography (FFA) uniquely provides detailed insights into retinal vascular dynamics and pathology, surpassing Color Fundus Photographs (CFP) in detecting microvascular abnormalities and perfusion status. However, the conventional invasive FFA involves discomfort and risks due to fluorescein dye injection, and it is meaningful but challenging to synthesize FFA images from non-invasive CFP. Previous studies primarily focused on FFA synthesis in a single disease category. In this work, we explore FFA synthesis in multiple diseases by devising a Diffusion-guided generative adversarial network, which introduces an adaptive and dynamic diffusion forward process into the discriminator and adds a category-aware representation enhancer. Moreover, to facilitate this research, we collect the first multi-disease CFP and FFA paired dataset, named the Multi-disease Paired Ocular Synthesis (MPOS) dataset, with four different fundus diseases. Experimental results show that our FFA synthesis network can generate better FFA images compared to state-of-the-art methods. Furthermore, we introduce a paired-modal diagnostic network to validate the effectiveness of synthetic FFA images in the diagnosis of multiple fundus diseases, and the results show that our synthesized FFA images with the real CFP images have higher diagnosis accuracy than that of the compared FFA synthesizing methods. Our research bridges the gap between non-invasive imaging and FFA, thereby offering promising prospects to enhance ophthalmic diagnosis and patient care, with a focus on reducing harm to patients through non-invasive procedures. Our dataset and code will be released to support further research in this field (https://github.com/whq-xxh/FFA-Synthesis). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14965v1-abstract-full').style.display = 'none'; document.getElementById('2410.14965v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ACMMM 24 MCHM</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.09436">arXiv:2410.09436</a> <span> [<a href="https://arxiv.org/pdf/2410.09436">pdf</a>, <a href="https://arxiv.org/ps/2410.09436">ps</a>, <a href="https://arxiv.org/format/2410.09436">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"> Sum Rate Maximization for Movable Antenna Enhanced Multiuser Covert Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Mao%2C+H">Haobin Mao</a>, <a href="/search/eess?searchtype=author&query=Pi%2C+X">Xiangyu Pi</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+X">Xiang-Gen Xia</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="2410.09436v2-abstract-short" style="display: inline;"> In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly de… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09436v2-abstract-full').style.display = 'inline'; document.getElementById('2410.09436v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.09436v2-abstract-full" style="display: none;"> In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly design the transmit beamforming and the positions of MAs at the AP. To solve the formulated non-convex optimization problem, we develop a block successive upper-bound minimization (BSUM) based algorithm, where the proximal distance algorithm (PDA) and the successive convex approximation (SCA) are employed to optimize the transmit beamforming and the MAs' positions, respectively. Simulation results show that the proposed MAs-aided system can significantly increase the covert sum rate via antenna position optimization as compared to conventional systems with fixed-position antennas (FPAs). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.09436v2-abstract-full').style.display = 'none'; document.getElementById('2410.09436v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 5 figures (subfigures included), submitted to an IEEE journal for possible publication</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08485">arXiv:2410.08485</a> <span> [<a href="https://arxiv.org/pdf/2410.08485">pdf</a>, <a href="https://arxiv.org/format/2410.08485">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"> Beyond GFVC: A Progressive Face Video Compression Framework with Adaptive Visual Tokens </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Chen%2C+B">Bolin Chen</a>, <a href="/search/eess?searchtype=author&query=Yin%2C+S">Shanzhi Yin</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Z">Zihan Zhang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+J">Jie Chen</a>, <a href="/search/eess?searchtype=author&query=Liao%2C+R">Ru-Ling Liao</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingyu Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+S">Shiqi Wang</a>, <a href="/search/eess?searchtype=author&query=Ye%2C+Y">Yan Ye</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.08485v1-abstract-short" style="display: inline;"> Recently, deep generative models have greatly advanced the progress of face video coding towards promising rate-distortion performance and diverse application functionalities. Beyond traditional hybrid video coding paradigms, Generative Face Video Compression (GFVC) relying on the strong capabilities of deep generative models and the philosophy of early Model-Based Coding (MBC) can facilitate the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08485v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08485v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08485v1-abstract-full" style="display: none;"> Recently, deep generative models have greatly advanced the progress of face video coding towards promising rate-distortion performance and diverse application functionalities. Beyond traditional hybrid video coding paradigms, Generative Face Video Compression (GFVC) relying on the strong capabilities of deep generative models and the philosophy of early Model-Based Coding (MBC) can facilitate the compact representation and realistic reconstruction of visual face signal, thus achieving ultra-low bitrate face video communication. However, these GFVC algorithms are sometimes faced with unstable reconstruction quality and limited bitrate ranges. To address these problems, this paper proposes a novel Progressive Face Video Compression framework, namely PFVC, that utilizes adaptive visual tokens to realize exceptional trade-offs between reconstruction robustness and bandwidth intelligence. In particular, the encoder of the proposed PFVC projects the high-dimensional face signal into adaptive visual tokens in a progressive manner, whilst the decoder can further reconstruct these adaptive visual tokens for motion estimation and signal synthesis with different granularity levels. Experimental results demonstrate that the proposed PFVC framework can achieve better coding flexibility and superior rate-distortion performance in comparison with the latest Versatile Video Coding (VVC) codec and the state-of-the-art GFVC algorithms. The project page can be found at https://github.com/Berlin0610/PFVC. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08485v1-abstract-full').style.display = 'none'; document.getElementById('2410.08485v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.07196">arXiv:2410.07196</a> <span> [<a href="https://arxiv.org/pdf/2410.07196">pdf</a>, <a href="https://arxiv.org/format/2410.07196">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> EEGUnity: Open-Source Tool in Facilitating Unified EEG Datasets Towards Large-Scale EEG Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Qin%2C+C">Chengxuan Qin</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+R">Rui Yang</a>, <a href="/search/eess?searchtype=author&query=You%2C+W">Wenlong You</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhige Chen</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Longsheng Zhu</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+M">Mengjie Huang</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Z">Zidong Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.07196v1-abstract-short" style="display: inline;"> The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07196v1-abstract-full').style.display = 'inline'; document.getElementById('2410.07196v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07196v1-abstract-full" style="display: none;"> The increasing number of dispersed EEG dataset publications and the advancement of large-scale Electroencephalogram (EEG) models have increased the demand for practical tools to manage diverse EEG datasets. However, the inherent complexity of EEG data, characterized by variability in content data, metadata, and data formats, poses challenges for integrating multiple datasets and conducting large-scale EEG model research. To tackle the challenges, this paper introduces EEGUnity, an open-source tool that incorporates modules of 'EEG Parser', 'Correction', 'Batch Processing', and 'Large Language Model Boost'. Leveraging the functionality of such modules, EEGUnity facilitates the efficient management of multiple EEG datasets, such as intelligent data structure inference, data cleaning, and data unification. In addition, the capabilities of EEGUnity ensure high data quality and consistency, providing a reliable foundation for large-scale EEG data research. EEGUnity is evaluated across 25 EEG datasets from different sources, offering several typical batch processing workflows. The results demonstrate the high performance and flexibility of EEGUnity in parsing and data processing. The project code is publicly available at github.com/Baizhige/EEGUnity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07196v1-abstract-full').style.display = 'none'; document.getElementById('2410.07196v1-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.03426">arXiv:2410.03426</a> <span> [<a href="https://arxiv.org/pdf/2410.03426">pdf</a>, <a href="https://arxiv.org/ps/2410.03426">ps</a>, <a href="https://arxiv.org/format/2410.03426">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"> Movable-Antenna Aided Secure Transmission for RIS-ISAC Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ma%2C+Y">Yaodong Ma</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+K">Kai Liu</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+Y">Yanming Liu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.03426v1-abstract-short" style="display: inline;"> Integrated sensing and communication (ISAC) systems have the issue of secrecy leakage when using the ISAC waveforms for sensing, thus posing a potential risk for eavesdropping. To address this problem, we propose to employ movable antennas (MAs) and reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) performance of ISAC systems, where an eavesdropping target poten… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03426v1-abstract-full').style.display = 'inline'; document.getElementById('2410.03426v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.03426v1-abstract-full" style="display: none;"> Integrated sensing and communication (ISAC) systems have the issue of secrecy leakage when using the ISAC waveforms for sensing, thus posing a potential risk for eavesdropping. To address this problem, we propose to employ movable antennas (MAs) and reconfigurable intelligent surface (RIS) to enhance the physical layer security (PLS) performance of ISAC systems, where an eavesdropping target potentially wiretaps the signals transmitted by the base station (BS). To evaluate the synergistic performance gain provided by MAs and RIS, we formulate an optimization problem for maximizing the sum-rate of the users by jointly optimizing the transmit/receive beamformers of the BS, the reflection coefficients of the RIS, and the positions of MAs at communication users, subject to a minimum communication rate requirement for each user, a minimum radar sensing requirement, and a maximum secrecy leakage to the eavesdropping target. To solve this non-convex problem with highly coupled variables, a two-layer penalty-based algorithm is developed by updating the penalty parameter in the outer-layer iterations to achieve a trade-off between the optimality and feasibility of the solution. In the inner-layer iterations, the auxiliary variables are first obtained with semi-closed-form solutions using Lagrange duality. Then, the receive beamformer filter at the BS is optimized by solving a Rayleigh-quotient subproblem. Subsequently, the transmit beamformer matrix is obtained by solving a convex subproblem. Finally, the majorization-minimization (MM) algorithm is employed to optimize the RIS reflection coefficients and the positions of MAs. Extensive simulation results validate the considerable benefits of the proposed MAs-aided RIS-ISAC systems in enhancing security performance compared to traditional fixed position antenna (FPA)-based systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.03426v1-abstract-full').style.display = 'none'; document.getElementById('2410.03426v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19420">arXiv:2409.19420</a> <span> [<a href="https://arxiv.org/pdf/2409.19420">pdf</a>, <a href="https://arxiv.org/format/2409.19420">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"> Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lingting Zhu</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Y">Yizheng Chen</a>, <a href="/search/eess?searchtype=author&query=Liu%2C+L">Lianli Liu</a>, <a href="/search/eess?searchtype=author&query=Xing%2C+L">Lei Xing</a>, <a href="/search/eess?searchtype=author&query=Yu%2C+L">Lequan Yu</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="2409.19420v1-abstract-short" style="display: inline;"> Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19420v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19420v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19420v1-abstract-full" style="display: none;"> Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI. We reveal two distinct types of features in multi-modality imaging, namely intra- and inter-modality features, and present a multi-sensor learning (MSL) framework to utilize the crossover inter-modality features for augmented multi-modality imaging. The MSL imaging approach breaks down the boundaries of traditional imaging modalities and allows for optimal hybridization of CT and MRI, which maximizes the use of sensory data. We showcase the effectiveness of our DMI strategy through synergetic CT-MRI brain imaging. The principle of DMI is quite general and holds enormous potential for various DMI applications across disciplines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19420v1-abstract-full').style.display = 'none'; document.getElementById('2409.19420v1-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 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">18 pages, 14 figures. Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19346">arXiv:2409.19346</a> <span> [<a href="https://arxiv.org/pdf/2409.19346">pdf</a>, <a href="https://arxiv.org/ps/2409.19346">ps</a>, <a href="https://arxiv.org/format/2409.19346">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"> Channel Estimation for Movable Antenna Aided Wideband Communication Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</a>, <a href="/search/eess?searchtype=author&query=Cao%2C+S">Songqi Cao</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+X">Xiang-Gen Xia</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="2409.19346v1-abstract-short" style="display: inline;"> Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (R… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19346v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19346v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19346v1-abstract-full" style="display: none;"> Movable antenna (MA) is an emerging technology that can significantly improve communication performance via the continuous adjustment of the antenna positions. To unleash the potential of MAs in wideband communication systems, acquiring accurate channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers, is a crucial issue. In this paper, we study the channel estimation problem for wideband MA systems. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at selected position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem with multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Moreover, we also devise an alternating refinement approach to further improve the accuracy of the estimated FRVs, DRV, and PRT. This is achieved by minimizing the discrepancy between the received pilots and those constructed by the estimated CSI, which can be efficiently carried out by using the gradient descent algorithm. Finally, simulation results demonstrate that both the SOMP-based channel estimation method and alternating refinement method can reconstruct the complete wideband CSI with high accuracy, where the alternating refinement method performs better despite a higher complexity. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19346v1-abstract-full').style.display = 'none'; document.getElementById('2409.19346v1-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 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/2409.19316">arXiv:2409.19316</a> <span> [<a href="https://arxiv.org/pdf/2409.19316">pdf</a>, <a href="https://arxiv.org/ps/2409.19316">ps</a>, <a href="https://arxiv.org/format/2409.19316">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"> Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+W">Wenyan Ma</a>, <a href="/search/eess?searchtype=author&query=Xiao%2C+Z">Zhenyu Xiao</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="2409.19316v1-abstract-short" style="display: inline;"> Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, whe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19316v1-abstract-full').style.display = 'inline'; document.getElementById('2409.19316v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19316v1-abstract-full" style="display: none;"> Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum signal-to-interference-plus-noise ratio (SINR) across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum signal-to-noise ratio (SNR) among users is derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channel state information (CSI), with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19316v1-abstract-full').style.display = 'none'; document.getElementById('2409.19316v1-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 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/2409.13278">arXiv:2409.13278</a> <span> [<a href="https://arxiv.org/pdf/2409.13278">pdf</a>, <a href="https://arxiv.org/format/2409.13278">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"> 6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UAV Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ren%2C+T">Tianshi Ren</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+X">Xianchao Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Ma%2C+W">Wenyan Ma</a>, <a href="/search/eess?searchtype=author&query=Gao%2C+X">Xiaozheng Gao</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="2409.13278v1-abstract-short" style="display: inline;"> Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13278v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13278v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13278v1-abstract-full" style="display: none;"> Cellular-connected unmanned aerial vehicle (UAV) communications is an enabling technology to transmit control signaling or payload data for UAVs through cellular networks. Due to the line-of-sight (LoS) dominant air-to-ground channels, efficient interference mitigation is crucial to UAV communications, while the conventional fixed-position antenna (FPA) arrays have limited degrees of freedom (DoFs) to suppress the interference between the UAV and its non-associated co-channel base stations (BSs). To address this challenge, we propose in this letter a new approach by utilizing the six-dimensional movable antenna (6DMA) arrays to enhance the interference mitigation for the UAV. Specifically, we propose an efficient block coordinate descent (BCD) algorithm to iteratively optimize the antenna position vector (APV), array rotation vector (ARV), receive beamforming vector, and associated BS of the UAV to maximize its signal-to-interference-plus-noise ratio (SINR). Numerical results show that the proposed 6DMA enhanced cellular-connected UAV communication can significantly outperform that with the traditional FPA arrays and other benchmark schemes in terms of interference mitigation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13278v1-abstract-full').style.display = 'none'; document.getElementById('2409.13278v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.11711">arXiv:2409.11711</a> <span> [<a href="https://arxiv.org/pdf/2409.11711">pdf</a>, <a href="https://arxiv.org/format/2409.11711">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"> LFIC-DRASC: Deep Light Field Image Compression Using Disentangled Representation and Asymmetrical Strip Convolution </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Feng%2C+S">Shiyu Feng</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+Y">Yun Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linwei Zhu</a>, <a href="/search/eess?searchtype=author&query=Kwong%2C+S">Sam Kwong</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="2409.11711v1-abstract-short" style="display: inline;"> Light-Field (LF) image is emerging 4D data of light rays that is capable of realistically presenting spatial and angular information of 3D scene. However, the large data volume of LF images becomes the most challenging issue in real-time processing, transmission, and storage. In this paper, we propose an end-to-end deep LF Image Compression method Using Disentangled Representation and Asymmetrical… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11711v1-abstract-full').style.display = 'inline'; document.getElementById('2409.11711v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.11711v1-abstract-full" style="display: none;"> Light-Field (LF) image is emerging 4D data of light rays that is capable of realistically presenting spatial and angular information of 3D scene. However, the large data volume of LF images becomes the most challenging issue in real-time processing, transmission, and storage. In this paper, we propose an end-to-end deep LF Image Compression method Using Disentangled Representation and Asymmetrical Strip Convolution (LFIC-DRASC) to improve coding efficiency. Firstly, we formulate the LF image compression problem as learning a disentangled LF representation network and an image encoding-decoding network. Secondly, we propose two novel feature extractors that leverage the structural prior of LF data by integrating features across different dimensions. Meanwhile, disentangled LF representation network is proposed to enhance the LF feature disentangling and decoupling. Thirdly, we propose the LFIC-DRASC for LF image compression, where two Asymmetrical Strip Convolution (ASC) operators, i.e. horizontal and vertical, are proposed to capture long-range correlation in LF feature space. These two ASC operators can be combined with the square convolution to further decouple LF features, which enhances the model ability in representing intricate spatial relationships. Experimental results demonstrate that the proposed LFIC-DRASC achieves an average of 20.5\% bit rate reductions comparing with the state-of-the-art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.11711v1-abstract-full').style.display = 'none'; document.getElementById('2409.11711v1-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 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/2409.10351">arXiv:2409.10351</a> <span> [<a href="https://arxiv.org/pdf/2409.10351">pdf</a>, <a href="https://arxiv.org/format/2409.10351">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LWC.2024.3485513">10.1109/LWC.2024.3485513 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Over-the-Air Computation via 2D Movable Antenna Array </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+N">Nianzu Li</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+P">Peiran Wu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Mei%2C+W">Weidong Mei</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="2409.10351v1-abstract-short" style="display: inline;"> Movable antenna (MA) has emerged as a promising technology for improving the performance of wireless communication systems, which enables local movement of the antennas to create more favorable channel conditions. In this letter, we advance its application for over-the-air computation (AirComp) network, where an access point is equipped with a two-dimensional (2D) MA array to aggregate wireless da… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10351v1-abstract-full').style.display = 'inline'; document.getElementById('2409.10351v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10351v1-abstract-full" style="display: none;"> Movable antenna (MA) has emerged as a promising technology for improving the performance of wireless communication systems, which enables local movement of the antennas to create more favorable channel conditions. In this letter, we advance its application for over-the-air computation (AirComp) network, where an access point is equipped with a two-dimensional (2D) MA array to aggregate wireless data from massive users. We aim to minimize the computation mean square error (CMSE) by jointly optimizing the antenna position vector (APV), the receive combining vector at the access point and the transmit coefficients from all users. To tackle this highly non-convex problem, we propose a two-loop iterative algorithm, where the particle swarm optimization (PSO) approach is leveraged to obtain a suboptimal APV in the outer loop while the receive combining vector and transmit coefficients are alternately optimized in the inner loop. Numerical results demonstrate that the proposed MA-enhanced AirComp network outperforms the conventional network with fixed-position antennas (FPAs). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10351v1-abstract-full').style.display = 'none'; document.getElementById('2409.10351v1-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 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">Journal ref:</span> IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 33-37, Jan. 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.08500">arXiv:2409.08500</a> <span> [<a href="https://arxiv.org/pdf/2409.08500">pdf</a>, <a href="https://arxiv.org/format/2409.08500">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"> Cross-conditioned Diffusion Model for Medical Image to Image Translation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Xing%2C+Z">Zhaohu Xing</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+S">Sicheng Yang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+S">Sixiang Chen</a>, <a href="/search/eess?searchtype=author&query=Ye%2C+T">Tian Ye</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+Y">Yijun Yang</a>, <a href="/search/eess?searchtype=author&query=Qin%2C+J">Jing Qin</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lei Zhu</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="2409.08500v1-abstract-short" style="display: inline;"> Multi-modal magnetic resonance imaging (MRI) provides rich, complementary information for analyzing diseases. However, the practical challenges of acquiring multiple MRI modalities, such as cost, scan time, and safety considerations, often result in incomplete datasets. This affects both the quality of diagnosis and the performance of deep learning models trained on such data. Recent advancements… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08500v1-abstract-full').style.display = 'inline'; document.getElementById('2409.08500v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.08500v1-abstract-full" style="display: none;"> Multi-modal magnetic resonance imaging (MRI) provides rich, complementary information for analyzing diseases. However, the practical challenges of acquiring multiple MRI modalities, such as cost, scan time, and safety considerations, often result in incomplete datasets. This affects both the quality of diagnosis and the performance of deep learning models trained on such data. Recent advancements in generative adversarial networks (GANs) and denoising diffusion models have shown promise in natural and medical image-to-image translation tasks. However, the complexity of training GANs and the computational expense associated with diffusion models hinder their development and application in this task. To address these issues, we introduce a Cross-conditioned Diffusion Model (CDM) for medical image-to-image translation. The core idea of CDM is to use the distribution of target modalities as guidance to improve synthesis quality while achieving higher generation efficiency compared to conventional diffusion models. First, we propose a Modality-specific Representation Model (MRM) to model the distribution of target modalities. Then, we design a Modality-decoupled Diffusion Network (MDN) to efficiently and effectively learn the distribution from MRM. Finally, a Cross-conditioned UNet (C-UNet) with a Condition Embedding module is designed to synthesize the target modalities with the source modalities as input and the target distribution for guidance. Extensive experiments conducted on the BraTS2023 and UPenn-GBM benchmark datasets demonstrate the superiority of our method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.08500v1-abstract-full').style.display = 'none'; document.getElementById('2409.08500v1-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">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">miccai24</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.10552">arXiv:2408.10552</a> <span> [<a href="https://arxiv.org/pdf/2408.10552">pdf</a>, <a href="https://arxiv.org/ps/2408.10552">ps</a>, <a href="https://arxiv.org/format/2408.10552">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LWC.2024.3490697">10.1109/LWC.2024.3490697 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Near-Field Multiuser Communications Aided by Movable Antennas </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Ding%2C+J">Jingze Ding</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhou%2C+Z">Zijian Zhou</a>, <a href="/search/eess?searchtype=author&query=Jiao%2C+B">Bingli Jiao</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="2408.10552v2-abstract-short" style="display: inline;"> This letter investigates movable antenna (MA)-aided downlink (DL) multiuser communication systems under the near-field channel condition, where both the base station (BS) and the users are equipped with MAs to fully exploit the degrees of freedom (DoFs) in antenna position optimization. We develop a general channel model to accurately describe the channel characteristics in the near-field region a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10552v2-abstract-full').style.display = 'inline'; document.getElementById('2408.10552v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.10552v2-abstract-full" style="display: none;"> This letter investigates movable antenna (MA)-aided downlink (DL) multiuser communication systems under the near-field channel condition, where both the base station (BS) and the users are equipped with MAs to fully exploit the degrees of freedom (DoFs) in antenna position optimization. We develop a general channel model to accurately describe the channel characteristics in the near-field region and formulate an MA-position optimization problem to minimize the BS's transmit power subject to users' individual rate constraints. To solve this problem, we propose a two-loop dynamic neighborhood pruning particle swarm optimization (DNPPSO) algorithm that significantly reduces the computational complexity as compared to the standard particle swarm optimization (PSO) algorithm while achieving similar performance. Simulation results validate the effectiveness and advantages of the proposed scheme in power-saving for near-field multiuser communications. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.10552v2-abstract-full').style.display = 'none'; document.getElementById('2408.10552v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This paper has been accepted by IEEE 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/2408.06789">arXiv:2408.06789</a> <span> [<a href="https://arxiv.org/pdf/2408.06789">pdf</a>, <a href="https://arxiv.org/ps/2408.06789">ps</a>, <a href="https://arxiv.org/format/2408.06789">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/LWC.2024.3403138">10.1109/LWC.2024.3403138 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Sum Rate Maximization for Movable Antenna Enabled Uplink NOMA </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Li%2C+N">Nianzu Li</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+P">Peiran Wu</a>, <a href="/search/eess?searchtype=author&query=Ning%2C+B">Boyu Ning</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06789v1-abstract-short" style="display: inline;"> Movable antenna (MA) has been recently proposed as a promising candidate technology for the next generation wireless communication systems due to its significant capability of reconfiguring wireless channels via antenna movement. In this letter, we study an MA-enabled uplink non-orthogonal multiple access (NOMA) system, where each user is equipped with a single MA. Our objective is to maximize the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06789v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06789v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06789v1-abstract-full" style="display: none;"> Movable antenna (MA) has been recently proposed as a promising candidate technology for the next generation wireless communication systems due to its significant capability of reconfiguring wireless channels via antenna movement. In this letter, we study an MA-enabled uplink non-orthogonal multiple access (NOMA) system, where each user is equipped with a single MA. Our objective is to maximize the users' sum rate by jointly optimizing the MAs' positions, the decoding order and the power control. To solve this non-convex problem, we equivalently transform it into two tractable subproblems. First, we use the successive convex approximation (SCA) to find a locally optimal solution for the antenna position optimization subproblem. Next, we derive the closed-form optimal solution of the decoding order and power control subproblem. Numerical results show that our proposed MA-enabled NOMA system can significantly enhance the sum rate compared to fixed-position antenna (FPA) systems and orthogonal multiple access (OMA) systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06789v1-abstract-full').style.display = 'none'; document.getElementById('2408.06789v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures. Accepted to IEEE Wireless Communications Letters</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> IEEE Wireless Communications Letters, vol. 13, no. 8, pp. 2140-2144, Aug. 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.20252">arXiv:2407.20252</a> <span> [<a href="https://arxiv.org/pdf/2407.20252">pdf</a>, <a href="https://arxiv.org/format/2407.20252">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"> Power Measurement Enabled Channel Autocorrelation Matrix Estimation for IRS-Assisted Wireless Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Yan%2C+G">Ge Yan</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2407.20252v1-abstract-short" style="display: inline;"> By reconfiguring wireless channels via passive signal reflection, intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-involved links. Prior works on IRS CSI acquisition mainly estimate IRS-cascaded channels based on th… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20252v1-abstract-full').style.display = 'inline'; document.getElementById('2407.20252v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.20252v1-abstract-full" style="display: none;"> By reconfiguring wireless channels via passive signal reflection, intelligent reflecting surface (IRS) can bring significant performance enhancement for wireless communication systems. However, such performance improvement generally relies on the knowledge of channel state information (CSI) for IRS-involved links. Prior works on IRS CSI acquisition mainly estimate IRS-cascaded channels based on the extra pilot signals received at the users/base station (BS) with time-varying IRS reflections, which, however, needs to modify the existing channel training/estimation protocols of wireless systems. To address this issue, we propose in this paper a new channel estimation scheme for IRS-assisted communication systems based on the received signal power measured at the user terminal, which is practically attainable without the need of changing the current protocol. Due to the lack of signal phase information in measured power, the autocorrelation matrix of the BS-IRS-user cascaded channel is estimated by solving an equivalent rank-minimization problem. To this end, a low-rank-approaching (LRA) algorithm is proposed by employing the fractional programming and alternating optimization techniques. To reduce computational complexity, an approximate LRA (ALRA) algorithm is also developed. Furthermore, these two algorithms are extended to be robust against the receiver noise and quantization error in power measurement. Simulation results are provided to verify the effectiveness of the proposed channel estimation algorithms as well as the IRS passive reflection design based on the estimated channel autocorrelation matrix. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.20252v1-abstract-full').style.display = 'none'; document.getElementById('2407.20252v1-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, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">17 pages, 16 figures, part of this work was presented at the IEEE Global Communications Conference Workshops 2023, Kuala Lumpur, Malaysia. arXiv admin note: text overlap with arXiv:2310.11038</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.17691">arXiv:2407.17691</a> <span> [<a href="https://arxiv.org/pdf/2407.17691">pdf</a>, <a href="https://arxiv.org/format/2407.17691">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="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shutao Zhang</a>, <a href="/search/eess?searchtype=author&query=Wen%2C+W">Wenkun Wen</a>, <a href="/search/eess?searchtype=author&query=Wu%2C+P">Peiran Wu</a>, <a href="/search/eess?searchtype=author&query=Huang%2C+H">Hongqing Huang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Liya Zhu</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Y">Yijia Guo</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+T">Tingting Yang</a>, <a href="/search/eess?searchtype=author&query=Xia%2C+M">Minghua Xia</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.17691v2-abstract-short" style="display: inline;"> Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17691v2-abstract-full').style.display = 'inline'; document.getElementById('2407.17691v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.17691v2-abstract-full" style="display: none;"> Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3GPP. This paper presents a system-level simulation framework for NB-IoT networks to evaluate their performance. The system-level simulator is structured into four parts: initialization, pre-generation, main simulation loop, and post-processing. Additionally, three essential features are investigated to enhance coverage, support massive connections, and ensure low power consumption, respectively. Simulation results demonstrate that the cumulative distribution function curves of the signal-to-interference-and-noise ratio fully comply with industrial standards. Furthermore, the throughput performance explains how NB-IoT networks realize massive connections at the cost of data rate. This work highlights its practical utility and paves the way for developing NB-IoT networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.17691v2-abstract-full').style.display = 'none'; document.getElementById('2407.17691v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.16404">arXiv:2407.16404</a> <span> [<a href="https://arxiv.org/pdf/2407.16404">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> <p class="title is-5 mathjax"> Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zhu%2C+S">Shuyang Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+Z">Ziqing Zhu</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Linghua Zhu</a>, <a href="/search/eess?searchtype=author&query=Ye%2C+Y">Yujian Ye</a>, <a href="/search/eess?searchtype=author&query=Bu%2C+S">Siqi Bu</a>, <a href="/search/eess?searchtype=author&query=Djokic%2C+S+Z">Sasa Z. Djokic</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.16404v1-abstract-short" style="display: inline;"> The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.16404v1-abstract-full').style.display = 'inline'; document.getElementById('2407.16404v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.16404v1-abstract-full" style="display: none;"> The analysis of decision-making process in electricity markets is crucial for understanding and resolving issues related to market manipulation and reduced social welfare. Traditional Multi-Agent Reinforcement Learning (MARL) method can model decision-making of generation companies (GENCOs), but faces challenges due to uncertainties in policy functions, reward functions, and inter-agent interactions. Quantum computing offers a promising solution to resolve these uncertainties, and this paper introduces the Quantum Multi-Agent Deep Q-Network (Q-MADQN) method, which integrates variational quantum circuits into the traditional MARL framework. The main contributions of the paper are: identifying the correspondence between market uncertainties and quantum properties, proposing the Q-MADQN algorithm for simulating electricity market bidding, and demonstrating that Q-MADQN allows for a more thorough exploration and simulates more potential bidding strategies of profit-oriented GENCOs, compared to conventional methods, without compromising computational efficiency. The proposed method is illustrated on IEEE 30-bus test network, confirming that it offers a more accurate model for simulating complex market dynamics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.16404v1-abstract-full').style.display = 'none'; document.getElementById('2407.16404v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">3 pages, 3 figures, plan for submitting to IEEE Power Engineering 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/2407.11413">arXiv:2407.11413</a> <span> [<a href="https://arxiv.org/pdf/2407.11413">pdf</a>, <a href="https://arxiv.org/format/2407.11413">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Distributed Prescribed-Time Convex Optimization: Cascade Design and Time-Varying Gain Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zuo%2C+G">Gewei Zuo</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lijun Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yujuan Wang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhiyong 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="2407.11413v2-abstract-short" style="display: inline;"> In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementation is divided into two parts: distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11413v2-abstract-full').style.display = 'inline'; document.getElementById('2407.11413v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11413v2-abstract-full" style="display: none;"> In this paper, we address the distributed prescribed-time convex optimization (DPTCO) problem for a class of nonlinear multi-agent systems (MASs) under undirected connected graph. A cascade design framework is proposed such that the DPTCO implementation is divided into two parts: distributed optimal trajectory generator design and local reference trajectory tracking controller design. The DPTCO problem is then transformed into the prescribed-time stabilization problem of a cascaded system. Changing Lyapunov function method and time-varying state transformation method together with the sufficient conditions are proposed to prove the prescribed-time stabilization of the cascaded system as well as the uniform boundedness of internal signals in the closed-loop systems. The proposed framework is then utilized to solve robust DPTCO problem for a class of chain-integrator MASs with external disturbances by constructing a novel variables and exploiting the property of time-varying gains. The proposed framework is further utilized to solve the adaptive DPTCO problem for a class of strict-feedback MASs with parameter uncertainty, in which backstepping method with prescribed-time dynamic filter is adopted. The descending power state transformation is introduced to compensate the growth of increasing rate induced by the derivative of time-varying gains in recursive steps and the high-order derivative of local reference trajectory is not required. Finally, theoretical results are verified by two numerical examples. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11413v2-abstract-full').style.display = 'none'; document.getElementById('2407.11413v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages,</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11408">arXiv:2407.11408</a> <span> [<a href="https://arxiv.org/pdf/2407.11408">pdf</a>, <a href="https://arxiv.org/format/2407.11408">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"> Prescribed-time Cooperative Output Regulation of Linear Heterogeneous Multi-agent Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zuo%2C+G">Gewei Zuo</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lijun Zhu</a>, <a href="/search/eess?searchtype=author&query=Wang%2C+Y">Yujuan Wang</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+Z">Zhiyong 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="2407.11408v2-abstract-short" style="display: inline;"> A finite-time protocol for a multi-agent systems (MASs) can guarantee the convergence of every agent in a finite time interval in contrast to the asymptotic convergence, but the settling time depends on the initial condition and design parameters and is inconsistent across the agents. In this paper, we study the prescribed-time cooperative output regulation (PTCOR) problem for a class of linear he… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11408v2-abstract-full').style.display = 'inline'; document.getElementById('2407.11408v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11408v2-abstract-full" style="display: none;"> A finite-time protocol for a multi-agent systems (MASs) can guarantee the convergence of every agent in a finite time interval in contrast to the asymptotic convergence, but the settling time depends on the initial condition and design parameters and is inconsistent across the agents. In this paper, we study the prescribed-time cooperative output regulation (PTCOR) problem for a class of linear heterogeneous MASs under a directed communication graph, where the settling time of every agent can be specified a priori and thus consistent. As a special case of PTCOR, the necessary and sufficient condition for prescribed-time output regulation of an individual system is first discussed. Then, the PTCOR problem is converted into two cascaded subsystem, where the first one composed of distributed estimate errors and local estimate errors and the second one is for local tracking errors. The criterion for prescribed-time stabilization of the cascaded system is proposed and is found to be different from that of traditional asymptotic stabilization of a cascaded system. Under the criterion and sufficient condition, the general PTCOR problem is studied in two scenarios including state feedback control and measurement output feedback control. In particular, a distributed prescribed-time observer for each subsystem is explicitly constructed to estimate the exosystem's state. Based on the observer, a distributed controller is proposed to achieve convergence of the regulated output to zero within a prescribed-time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11408v2-abstract-full').style.display = 'none'; document.getElementById('2407.11408v2-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 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">None</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.11397">arXiv:2407.11397</a> <span> [<a href="https://arxiv.org/pdf/2407.11397">pdf</a>, <a href="https://arxiv.org/format/2407.11397">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"> Adaptive Event-triggered Control with Sampled Transmitted Output and Controller Dynamics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Zuo%2C+G">Gewei Zuo</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lijun Zhu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.11397v1-abstract-short" style="display: inline;"> The event-triggered control with intermittent output can reduce the communication burden between the controller and plant side over the network. It has been exploited for adaptive output feedback control of uncertain nonlinear systems in the literature, however the controller must partially reside at the plant side where the computation capacity is required. In this paper, all controller component… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11397v1-abstract-full').style.display = 'inline'; document.getElementById('2407.11397v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.11397v1-abstract-full" style="display: none;"> The event-triggered control with intermittent output can reduce the communication burden between the controller and plant side over the network. It has been exploited for adaptive output feedback control of uncertain nonlinear systems in the literature, however the controller must partially reside at the plant side where the computation capacity is required. In this paper, all controller components are moved to the controller side and their dynamics use sampled states rather than continuous one with the benefit of directly estimating next triggering instance of some conditions and avoiding constantly checking event condition at the controller side. However, these bring two major challenges. First, the virtual input designed in the dynamic filtering technique for the stabilization is no longer differentiable. Second, the plant output is sampled to transmit at plant side and sampled again at controller side to construct the controller, and the two asynchronous samplings make the analysis more involving. This paper solves these two issues by introducing a new state observer to simplify the adaptive law, a set of continuous companion variables for stability analysis and a new lemma quantifying the error bound between actual output signal and sampled transmitted output. It is theoretically guaranteed that all internal signals in the closed-loop system are semiglobally bounded and the output is practically stabilized to the origin. Finally, the numerical simulation illustrates the effectiveness of proposed scheme. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.11397v1-abstract-full').style.display = 'none'; document.getElementById('2407.11397v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 10 gigures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.10986">arXiv:2407.10986</a> <span> [<a href="https://arxiv.org/pdf/2407.10986">pdf</a>, <a href="https://arxiv.org/format/2407.10986">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="Networking and Internet Architecture">cs.NI</span> </div> </div> <p class="title is-5 mathjax"> Integrating Base Station with Intelligent Surface for 6G Wireless Networks: Architectures, Design Issues, and Future Directions </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Huang%2C+Y">Yuwei Huang</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Lipeng Zhu</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="2407.10986v2-abstract-short" style="display: inline;"> Intelligent surface (IS) is envisioned as a promising technology for the sixth-generation (6G) wireless networks, which can effectively reconfigure the wireless propagation environment via dynamically controllable signal reflection/transmission. In particular, integrating passive intelligent surface (IS) into the base station (BS) is a novel solution to enhance the wireless network throughput and… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10986v2-abstract-full').style.display = 'inline'; document.getElementById('2407.10986v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.10986v2-abstract-full" style="display: none;"> Intelligent surface (IS) is envisioned as a promising technology for the sixth-generation (6G) wireless networks, which can effectively reconfigure the wireless propagation environment via dynamically controllable signal reflection/transmission. In particular, integrating passive intelligent surface (IS) into the base station (BS) is a novel solution to enhance the wireless network throughput and coverage both cost-effectively and energyefficiently. In this article, we provide an overview of IS-integrated BSs for wireless networks, including their motivations, practical architectures, and main design issues. Moreover, numerical results are presented to compare the performance of different IS-integrated BS architectures as well as the conventional BS without IS. Finally, promising directions are pointed out to stimulate future research on IS-BS/terminal integration in wireless networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10986v2-abstract-full').style.display = 'none'; document.getElementById('2407.10986v2-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 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted by IEEE Wireless Communications. 5 figures, 1 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.07306">arXiv:2407.07306</a> <span> [<a href="https://arxiv.org/pdf/2407.07306">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Medical Physics">physics.med-ph</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"> Electrical Impedance Tomography Based Closed-loop Tumor Treating Fields in Dynamic Lung Tumors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/eess?searchtype=author&query=Wang%2C+M">Minmin Wang</a>, <a href="/search/eess?searchtype=author&query=Xie%2C+X">Xu Xie</a>, <a href="/search/eess?searchtype=author&query=Guo%2C+Y">Yuxi Guo</a>, <a href="/search/eess?searchtype=author&query=Zhu%2C+L">Liying Zhu</a>, <a href="/search/eess?searchtype=author&query=Lan%2C+Y">Yue Lan</a>, <a href="/search/eess?searchtype=author&query=Yang%2C+H">Haitang Yang</a>, <a href="/search/eess?searchtype=author&query=Pan%2C+Y">Yun Pan</a>, <a href="/search/eess?searchtype=author&query=Chen%2C+G">Guangdi Chen</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+S">Shaomin Zhang</a>, <a href="/search/eess?searchtype=author&query=Zhang%2C+M">Maomao 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="2407.07306v1-abstract-short" style="display: inline;"> Tumor Treating Fields (TTFields) is a non-invasive anticancer modality that utilizes alternating electric fields to disrupt cancer cell division and growth. While generally well-tolerated with minimal side effects, traditional TTFields therapy for lung tumors faces challenges due to the influence of respiratory motion. We design a novel closed-loop TTFields strategy for lung tumors by incorporatin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07306v1-abstract-full').style.display = 'inline'; document.getElementById('2407.07306v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.07306v1-abstract-full" style="display: none;"> Tumor Treating Fields (TTFields) is a non-invasive anticancer modality that utilizes alternating electric fields to disrupt cancer cell division and growth. While generally well-tolerated with minimal side effects, traditional TTFields therapy for lung tumors faces challenges due to the influence of respiratory motion. We design a novel closed-loop TTFields strategy for lung tumors by incorporating electrical impedance tomography (EIT) for real-time respiratory phase monitoring and dynamic parameter adjustments. Furthermore, we conduct theoretical analysis to evaluate the performance of the proposed method using the lung motion model. Compared to conventional TTFields settings, we observed that variations in the electrical conductivity of lung during different respiratory phases led to a decrease in the average electric field intensity within lung tumors, transitioning from end-expiratory (1.08 V/cm) to end-inspiratory (0.87 V/cm) phases. Utilizing our proposed closed-Loop TTFields approach at the same dose setting (2400 mA, consistent with the traditional TTFields setting), we can achieve a higher and consistent average electric field strength at the tumor site (1.30 V/cm) across different respiratory stages. Our proposed closed-loop TTFields method has the potential to improved lung tumor therapy by mitigating the impact of respiratory motion. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.07306v1-abstract-full').style.display = 'none'; document.getElementById('2407.07306v1-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 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">7 pages, 5 figures</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a 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