EARL: Energy-Aware Adaptive Antenna Control with Reinforcement Learning in O-RAN Cell-Free Massive MIMO Networks

TL;DR

This paper introduces EARL, a reinforcement learning framework for adaptive antenna control in cell-free massive MIMO networks, significantly reducing energy consumption while maintaining performance within real-time constraints.

Contribution

The paper presents a novel RL-based adaptive antenna control method for O-RAN cell-free MIMO, achieving substantial energy savings and real-time operation.

Findings

Power savings of up to 81% over full-on baseline.

Operates within 220 ms, meeting near-real-time requirements.

Greedy refinement halves power consumption with 2 s runtime.

Abstract

Cell-free massive multi-input multi-output (MIMO) promises uniform high performance across the network, but also brings a high energy cost due to joint transmission from distributed radio units (RUs) and centralized processing in the cloud. Leveraging the resource-sharing capabilities of Open Radio Access Network (O-RAN), we propose EARL, an energy-aware adaptive antenna control framework based on reinforcement learning. EARL dynamically configures antenna elements in RUs to minimize radio, optical fronthaul, and cloud processing power consumption while meeting user spectral efficiency demands. Numerical results show power savings of up to 81% and 50% over full-on and heuristic baselines, respectively. The RL-based approach operates within 220 ms, satisfying O-RAN's near-real-time limit, and a greedy refinement further halves power consumption at a 2 s runtime.