Double-Layer Power Control for Mobile Cell-Free XL-MIMO with Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a novel double-layer power control method using multi-agent reinforcement learning for cell-free XL-MIMO systems, significantly improving spectral efficiency and managing interference in dynamic environments.

Contribution

It proposes a new double-layer power control architecture combined with MARL, enhancing spectral efficiency and interference suppression in XL-MIMO systems.

Findings

Double-layer architecture improves spectral efficiency by nearly 24%.

MARL-based algorithm balances spectral efficiency and convergence time.

Proposed method outperforms single-layer architecture in dynamic environments.

Abstract

Cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for enabling future wireless communication systems. Significant attention has been generated by its considerable advantages in augmenting degrees of freedom. In this paper, we first investigate a CF XL-MIMO system with base stations equipped with XL-MIMO panels under a dynamic environment. Then, we propose an innovative multi-agent reinforcement learning (MARL)-based power control algorithm that incorporates predictive management and distributed optimization architecture, which provides a dynamic strategy for addressing high-dimension signal processing problems. Specifically, we compare various MARL-based algorithms, which shows that the proposed MARL-based algorithm effectively strikes a balance between spectral efficiency (SE) performance and convergence time. Moreover, we consider a double-layer power control architecture based on the large-scale fading coefficients between antennas to suppress interference within dynamic systems. Compared to the single-layer architecture, the results obtained unveil that the proposed double-layer architecture has a nearly24% SE performance improvement, especially with massive antennas and smaller antenna spacing.