DRL-Based AP Selection in Downlink Cell-Free Massive MIMO Network With Pilot Contamination

作     者：Gao, Zhichao Zhang, Qian Liu, Ju Du, Zhengfeng Li, Yunxiao 

作者机构：Shandong Univ Sch Informat Sci & Engn Qingdao 266237 Peoples R China 

出 版 物：《IEEE COMMUNICATIONS LETTERS》 (IEEE Commun Lett)

年 卷 期：2024年第28卷第6期

页      面：1432-1436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] 

基　　金：National Natural Science Foundation of China 

主　　题：Massive MIMO Downlink Uplink Signal processing algorithms Contamination Training Optimization Cell-free massive multiple-input multiple-output deep reinforcement learning access point selection 

摘      要：Cell-free massive multiple-input multiple-output (MIMO) network includes numerous geographically distributed access points (APs) serving users through coherent transmission and reception. To achieve scalability, each user should be assigned a personalized cluster of APs. In this letter, we propose a deep reinforcement learning (DRL)-based approach to determine the cluster of APs for each user while satisfying constraints on minimum rates for all users, considering practical concerns such as pilot contamination and statistical channel state information (CSI). Simulation results demonstrate that the proposed DRL-based AP selection scheme outperforms other conventional schemes.