Near-Field Spot Beamfocusing: A Correlation-Aware Transfer Learning Approach

作     者：Fallah, Mohammad Amir Monemi, Mehdi Rasti, Mehdi Latva-aho, Matti 

作者机构：Payame Noor Univ PNU Dept Engn Tehran 19569 Iran Univ Oulu Ctr Wireless Commun CWC Oulu 90570 Finland 

出 版 物：《IEEE TRANSACTIONS ON MOBILE COMPUTING》 (IEEE Trans. Mob. Comput.)

年 卷 期：2025年第24卷第5期

页      面：3935-3949页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Business Finland [8002/31/2022] Research Council of Finland [346208, 336449] 

主　　题：Training Array signal processing Antenna arrays Transmission line matrix methods Phased arrays Correlation Transfer learning Aperture antennas Wireless communication Transmitting antennas Spot beamfocusing near-field reinforcement learning transfer learning policy propagation policy blending quasi-liquid layer phase distribution image 

摘      要：Three-dimensional (3D) spot beamfocusing (SBF), in contrast to conventional angular-domain beamforming, concentrates radiating power within a very small volume in both radial and angular domains in the near-field zone. Recently the implementation of channel-state-information (CSI)-independent machine learning (ML)-based approaches have been developed for effective SBF using extremely large-scale programmable metasurface (ELPMs). These methods involve dividing the ELPMs into subarrays and independently training them with Deep Reinforcement Learning to jointly focus the beam at the desired focal point (DFP). This paper explores near-field SBF using ELPMs, addressing challenges associated with lengthy training times resulting from independent training of subarrays. To achieve a faster CSI-independent solution, inspired by the correlation between the beamfocusing matrices of the subarrays, we leverage transfer learning techniques. First, we introduce a novel similarity criterion based on the phase distribution image (PDI) of subarray apertures. Then we devise a subarray policy propagation scheme that transfers the knowledge from trained to untrained subarrays. We further enhance learning by introducing quasi-liquid layers as a revised version of the adaptive policy reuse technique. We show through simulations that the proposed scheme improves the training speed about 5 times. Furthermore, for dynamic DFP management, we devised a DFP policy blending process, which augments the convergence rate up to 8-fold.