Long-Term CSI-based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning

Ren, Hong, Pan, Cunhua, Wang, Liang, Liu, Wang, Kou, Zhoubin and Wang, Kezhi (2022) Long-Term CSI-based Design for RIS-Aided Multiuser MISO Systems Exploiting Deep Reinforcement Learning. IEEE Communications Letters, 26 (3). pp. 567-571. ISSN 1089-7798

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Official URL: https://doi.org/10.1109/LCOMM.2021.3140155


In this paper, we study the transmission design for reconfigurable intelligent surface (RIS)-aided multiuser communication networks. Different from most of the existing contributions, we consider long-term CSI-based transmission design, where both the beamforming vectors at the base station (BS) and the phase shifts at the RIS are designed based on long-term CSI, which can significantly reduce the channel estimation overhead. Due to the lack of explicit ergodic data rate expression, we propose a novel deep deterministic policy gradient (DDPG) based algorithm to solve the optimization problem, which was trained by using the channel vectors generated in an offline manner. Simulation results demonstrate that the achievable net throughput is higher than that achieved by the conventional instantaneous-CSI based scheme when taking the channel estimation overhead into account.

Item Type: Article
Additional Information: Funding information: This work was supported in part by the National Key Research and Development Project under Grant 2019YFE0123600, National Natural Science Foundation of China (62101128) and Basic Research Project of Jiangsu Provincial Department of Science and Technology (BK20210205).
Uncontrolled Keywords: Array signal processing, Channel estimation, Coherence time, deep reinforcement learning, intelligent reflecting surface (IRS), Interference, Precoding, Reconfigurable intelligent surface (RIS), Rician channels, Signal to noise ratio
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 19 Jan 2022 12:05
Last Modified: 08 Apr 2022 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48204

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