Joint Precoding and AP Selection for Energy Efficient RIS-aided Cell-Free Massive MIMO Using Multi-agent Reinforcement Learning

TL;DR

This paper proposes a novel multi-agent reinforcement learning approach for joint precoding and AP selection in RIS-aided cell-free massive MIMO systems, significantly improving energy efficiency while managing computational complexity.

Contribution

It introduces a double-layer MARL scheme with fuzzy logic acceleration and adaptive AP selection for energy-efficient RIS-aided CF mMIMO, addressing complexity and power consumption challenges.

Findings

Achieves 85% EE improvement over ZF method

Faster convergence with FL-based MARL

Trade-off between spectral efficiency and energy consumption

Abstract

Cell-free (CF) massive multiple-input multiple-output (mMIMO) and reconfigurable intelligent surface (RIS) are two advanced transceiver technologies for realizing future sixth-generation (6G) networks. In this paper, we investigate the joint precoding and access point (AP) selection for energy efficient RIS-aided CF mMIMO system. To address the associated computational complexity and communication power consumption, we advocate for user-centric dynamic networks in which each user is served by a subset of APs rather than by all of them. Based on the user-centric network, we formulate a joint precoding and AP selection problem to maximize the energy efficiency (EE) of the considered system. To solve this complex nonconvex problem, we propose an innovative double-layer multi-agent reinforcement learning (MARL)-based scheme. Moreover, we propose an adaptive power threshold-based AP selection scheme to further enhance the EE of the considered system. To reduce the computational complexity of the RIS-aided CF mMIMO system, we introduce a fuzzy logic (FL) strategy into the MARL scheme to accelerate convergence. The simulation results show that the proposed FL-based MARL cooperative architecture effectively improves EE performance, offering a 85\% enhancement over the zero-forcing (ZF) method, and achieves faster convergence speed compared with MARL. It is important to note that increasing the transmission power of the APs or the number of RIS elements can effectively enhance the spectral efficiency (SE) performance, which also leads to an increase in power consumption, resulting in a non-trivial trade-off between the quality of service and EE performance.