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

arXiv: https://arxiv.org/abs/2309.17079 · HTML: https://mlsanity.com/paper/2309.17079/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Selective Experience Sharing in Reinforcement Learning Enhances Interference Management](https://mlsanity.com/paper/2501.15735/) (2025)

## Abstract (arXiv)

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.