Joint Beamforming with Extremely Large Scale RIS: A Sequential Multi-Agent A2C Approach

TL;DR

This paper introduces a deep reinforcement learning method for joint beamforming in large-scale RIS-assisted MU-MIMO systems, effectively handling discrete phases, imperfect CSI, and user channel correlations.

Contribution

It proposes a novel sequential multi-agent A2C algorithm that reduces computational complexity and improves spectral efficiency over traditional methods in large RIS scenarios.

Findings

Lower computational complexity than benchmarks

Better spectral efficiency than zero-forcing beamformer

Robust to medium channel estimation errors

Abstract

It is a challenging problem to jointly optimize the base station (BS) precoding matrix and the reconfigurable intelligent surface (RIS) phases simultaneously in a RIS-assisted multiple-user multiple-input-multiple-output (MU-MIMO) scenario when the size of the RIS becomes extremely large. In this paper, we propose a deep reinforcement learning algorithm called sequential multi-agent advantage actor-critic (A2C) to solve this problem. In addition, the discrete phase of RISs, imperfect channel state information (CSI), and channel correlations between users are taken into consideration. The computational complexity is also analyzed, and the performance of the proposed algorithm is compared with the zero-forcing (ZF) beamformer in terms of the sum spectral efficiency (SE). It is noted that the computational complexity of the proposed algorithm is lower than the benchmark, while the performance is better than the benchmark. Throughout simulations, it is also found that the proposed algorithm is robust to medium channel estimation error.