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

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...

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Bibliographic Details
Published in:IEEE communications letters 2024-06, Vol.28 (6), p.1432-1436
Main Authors: Gao, Zhichao, Zhang, Qian, Liu, Ju, Du, Zhengfeng, Li, Yunxiao
Format: Article
Language:English
Subjects:
access point selection
Cell-free
Clusters
Contamination
deep reinforcement learning
Downlink
Geographical distribution
Massive MIMO
massive multiple-input multiple-output
MIMO communication
Optimization
Signal processing algorithms
Training
Uplink
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Summary: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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2024.3387095