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Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems via Deep Reinforcement Learning

Eskandari, Mahdi, Zhu, Huiling, Shojaeifard, Arman, Wang, Jiangzhou (2023) Statistical CSI-based Beamforming for RIS-Aided Multiuser MISO Systems via Deep Reinforcement Learning. IEEE Wireless Communications Letters, 13 (2). pp. 570-574. ISSN 2162-2337. E-ISSN 2162-2345. (doi:10.1109/LWC.2023.3336999) (KAR id:104067)

Abstract

This letter presents a novel joint beamforming algorithm for reconfigurable intelligent surfaces (RIS) in multiuser multiple-input single-output (MISO) wireless communications. At first, by utilizing statistical channel state information (CSI) instead of instantaneous CSI, we significantly reduce channel estimation overhead. Then, the optimization of beamforming weights is accomplished using the proximal policy optimization (PPO) algorithm, a well-established actor-critic-based reinforcement learning (RL) approach. The impact of system parameters on user sum rate is also analyzed through simulations. The results show the PPO algorithm outperforms the existing methods by combining beamforming techniques with statistical CSI.

Item Type: Article
DOI/Identification number: 10.1109/LWC.2023.3336999
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Mahdi Eskandari
Date Deposited: 24 Nov 2023 18:11 UTC
Last Modified: 04 May 2024 03:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/104067 (The current URI for this page, for reference purposes)

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