Deep Reinforcement Learning-Based Dynamic Resource Allocation in Cell-Free Massive MIMO

TL;DR

This paper introduces a deep reinforcement learning framework to optimize power allocation and antenna activation in cell-free massive MIMO systems, significantly improving energy efficiency and reducing computation time.

Contribution

It proposes a novel DRL-based approach that transforms complex joint optimization into a scalable learning task for CFmMIMO resource management.

Findings

Achieves 50% energy efficiency improvement in simulations.

Reduces run time by 3350 times compared to traditional methods.

Effectively scales to systems with 40 APs and 20 users.

Abstract

In this paper, we consider power allocation and antenna activation of cell-free massive multiple-input multiple-output (CFmMIMO) systems. We first derive closed-form expressions for the system spectral efficiency (SE) and energy efficiency (EE) as functions of the power allocation coefficients and the number of active antennas at the access points (APs). Then, we aim to enhance the EE through jointly optimizing antenna activation and power control. This task leads to a non-convex and mixed-integer design problem with high-dimensional design variables. To address this, we propose a novel DRL-based framework, in which the agent learns to map large-scale fading coefficients to AP activation ratio, antenna coefficient, and power coefficient. These coefficients are then employed to determine the number of active antennas per AP and the power factors assigned to users based on closed-form expressions. By optimizing these parameters instead of directly controlling antenna selection and power allocation, the proposed method transforms the intractable optimization into a low-dimensional learning task. Our extensive simulations demonstrate the efficiency and scalability of the proposed scheme. Specifically, in a CFmMIMO system with 40 APs and 20 users, it achieves a 50% EE improvement and 3350 times run time reduction compared to the conventional sequential convex approximation method.