Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems

TL;DR

This paper proposes a distributed multi-agent reinforcement learning approach with fuzzy logic to optimize joint precoding and phase shifts in RIS-aided cell-free massive MIMO systems, enhancing spectral efficiency with reduced complexity.

Contribution

It introduces a novel FL-based MARL algorithm for joint precoding and phase shift optimization that requires only local information, unlike traditional methods.

Findings

Reduces computational complexity compared to conventional MARL methods.

Achieves similar spectral efficiency performance as existing approaches.

Effectively utilizes RIS to mitigate propagation environment issues.

Abstract

Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss. To overcome this issue, we introduce the reconfigurable intelligent surface (RIS) into the CF mMIMO system as a low-cost and power-efficient solution. In this paper, we focus on optimizing the joint precoding design of the RIS-aided CF mMIMO system to maximize the sum SE. This involves optimizing the precoding matrix at the APs and the reflection coefficients at the RIS. To tackle this problem, we propose a fully distributed multi-agent reinforcement learning (MARL) algorithm that incorporates fuzzy logic (FL). Unlike conventional approaches that rely on alternating optimization techniques, our FL-based MARL algorithm only requires local channel state information, which reduces the need for high backhaul capacity. Simulation results demonstrate that our proposed FL-MARL algorithm effectively reduces computational complexity while achieving similar performance as conventional MARL methods.