Uplink Power Control for Extremely Large-Scale MIMO with Multi-Agent Reinforcement Learning and Fuzzy Logic

TL;DR

This paper introduces novel multi-agent reinforcement learning and fuzzy logic-based architectures for uplink power control in cell-free XL-MIMO systems, improving spectral efficiency and reducing computational complexity.

Contribution

It proposes two new fuzzy logic integrated MARL architectures for power control in XL-MIMO, enhancing performance over traditional methods.

Findings

Fuzzy logic methods outperform conventional MARL in computational complexity.

SE performance with MR combining surpasses traditional MARL.

Proposed architectures are effective for uplink power optimization.

Abstract

In this paper, we investigate the uplink transmit power optimization problem in cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Instead of applying the traditional methods, we propose two signal processing architectures: the centralized training and centralized execution with fuzzy logic as well as the centralized training and decentralized execution with fuzzy logic, respectively, which adopt the amalgamation of multi-agent reinforcement learning (MARL) and fuzzy logic to solve the design problem of power control for the maximization of the system spectral efficiency (SE). Furthermore, the uplink performance of the system adopting maximum ratio (MR) combining and local minimum mean-squared error (L-MMSE) combining is evaluated. Our results show that the proposed methods with fuzzy logic outperform the conventional MARL-based method and signal processing methods in terms of computational complexity. Also, the SE performance under MR combining is even better than that of the conventional MARL-based method.