DRL-Based AP Selection in Downlink Cell-Free Massive MIMO Network With Pilot Contamination
Zhichao Gao, Qian Zhang, Ju Liu, Zhengfeng Du, Yunxiao Li
Abstract
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.
Topics & Concepts
Computer scienceTelecommunications linkMIMOChannel state informationComputer networkScalabilityBase stationTransmission (telecommunications)Reinforcement learningSelection (genetic algorithm)Channel (broadcasting)Distributed computingWirelessTelecommunicationsMachine learningDatabaseAdvanced MIMO Systems OptimizationEnergy Harvesting in Wireless NetworksAdvanced Wireless Communication Technologies