Deep Learning-Empowered Movable-Antenna Position Optimization with Partial CSI
Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（电子科技大学无线通信国家重点实验室）；School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳）理学院）；Department of Communication Systems, KTH Royal Institute of Technology（瑞典皇家理工学院通信系统系）
Lele Lu, Weidong Mei, Xin Wei, Ruixi Feng, Haocheng Hua, Zhi Chen, Boyu Ning, Emil Björnson
Movable antennas (MAs) are a promising technology to improve wireless data rates by dynamically adjusting their positions to avoid deep fading. However, finding the optimal MA positions requires full channel state information (CSI) for all possible locations within the movement region, creating massive channel estimation overhead. This paper proposes a deep neural network (DNN)-based learning framework to predict the optimal positions of multiple transmit MAs in a multi-user multiple-input single-output (MISO) system, entirely bypassing explicit channel estimation.First, we analyze a single-user MISO case, revealing a complex, highly nonlinear mapping between the optimal MA positions and the channel power gains from a specific subset of locations in the transmit region to the user. Because this mapping cannot be mathematically characterized for practical channel models, we train a DNN via supervised learning to capture it. The pre-trained DNN can then determine optimized MA positions in real-time relying only on partial power measurements from the transmit region.Extending this to multi-user scenarios is challenging due to complex rate expressions and the lack of globally optimal position solutions to use as training labels. To overcome this, we develop an unsupervised training framework that directly maximizes the multi-user sum-rate. This framework utilizes an attention-based architecture to extract latent features from the partial channel measurements and effectively manage inter-user interference. Simulation results show that our proposed approach achieves near-optimal performance in single-user systems and surpasses conventional CSI-based alternating optimization algorithms in multi-user environments.
Precoding design exploiting deep learning methods has been widely studied for multiuser multiple-input multiple-output (MU-MIMO) systems. However, conventional neural precoding design applies black-box-based neural networks which are less interpretable. In this paper, we propose a deep learning-based precoding method based on an interpretable design of a neural precoding network, namely iPNet. In particular, the iPNet mimics the classic minimum mean-squared error (MMSE) precoding and approximates the matrix inversion in the design of the neural network architecture. Specifically, the proposed …
Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness
City University of Hong Kong（香港城市大学）；Microsoft Research Asia（微软亚洲研究院）；Shaanxi Key Laboratory of Deep Space Exploration and Intelligent Information Technology, the School of Information and Communications Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University（西安交通大学电子与信息工程学院信息通信工程系深空探测与智能信息技术陕西省重点实验室）；National Mobile Communications Research Laboratory, Southeast University（东南大学移动通信国家重点实验室）；The Chinese University of Hong Kong（香港中文大学）
Transmissive RIS Transceiver-Empowered ISAC Systems: Energy Efficiency Optimization for Perfect and Imperfect CSI
Shanghai Jiao Tong University（上海交通大学）；Dalian University of Technology（大连理工大学）；East China Normal University（华东师范大学）；Xi’an Jiaotong University（西安交通大学）；Jiangnan University（江南大学）
The realization of practical intelligent reflecting surface (IRS)-assisted multi-user communication (IRS-MUC) systems critically depends on the proper beamforming design exploiting accurate channel state information (CSI). However, channel estimation (CE) in IRS-MUC systems requires a significantly large training overhead due to the numerous reflection elements involved in IRS. In this paper, we adopt a deep learning approach to implicitly learn the historical channel features and directly predict the IRS phase shifts for the next time slot to maximize the average achievable sum-rate of an IRS…
Unlocking the Potential of Movable Antennas: General and Practical Antenna Position Optimization
National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China（电子科技大学无线通信全国重点实验室）；Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电气与计算机工程系）
The accurate estimation of Channel State Information (CSI) is of crucial importance for the successful operation of Multiple-Input Multiple-Output (MIMO) communication systems, especially in a Multi-User (MU) time-varying environment and when employing the emerging technology of Reconfigurable Intelligent Surfaces (RISs). Their predominantly passive nature renders the estimation of the channels involved in the user-RIS-base station link a quite challenging problem. Moreover, the time-varying nature of most of the realistic wireless channels drives up the cost of real-time channel tracking sign…
Exploiting Movable-Element STARS for Rate Splitting Multiple Access
Tongling University（铜陵大学）；University of Oulu（奥卢大学）；Queen’s University Belfast（贝尔法斯特女王大学）；Qilu Institute of Technology（齐鲁工业大学）；University of Luxembourg（卢森堡大学）
