Joint Power Allocation and Phase Shift Design for Stacked Intelligent Metasurfaces-aided Cell-Free Massive MIMO Systems with MARL

TL;DR

This paper proposes a novel MARL-based approach to jointly optimize power allocation and phase shifts in SIM-aided CF mMIMO systems, significantly enhancing spectral efficiency and energy efficiency.

Contribution

It introduces the NVR-MAPPO algorithm for distributed optimization of power and phase shifts, a novel approach in SIM-aided CF mMIMO systems.

Findings

NVR-MAPPO outperforms existing methods in sum SE.

The proposed method improves robustness across scenarios.

Energy efficiency is enhanced with SIM integration.

Abstract

Cell-free (CF) massive multiple-input multiple-output (mMIMO) systems offer high spectral efficiency (SE) through multiple distributed access points (APs). However, the large number of antennas increases power consumption. We propose incorporating stacked intelligent metasurfaces (SIM) into CF mMIMO systems as a cost-effective, energy-efficient solution. This paper focuses on optimizing the joint power allocation of APs and the phase shift of SIMs to maximize the sum SE. To address this complex problem, we introduce a fully distributed multi-agent reinforcement learning (MARL) algorithm. Our novel algorithm, the noisy value method with a recurrent policy in multi-agent policy optimization (NVR-MAPPO), enhances performance by encouraging diverse exploration under centralized training and decentralized execution. Simulations demonstrate that NVR-MAPPO significantly improves sum SE and robustness across various scenarios.