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

TL;DR

EARL is proposed, an energy-aware adaptive antenna control framework based on reinforcement learning that dynamically configures antenna elements in RUs to minimize radio, optical fronthaul, and cloud processing power consumption while meeting user spectral efficiency demands.

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.

Figures (3)

• Figure 1: Average power consumption comparison under different SE thresholds.
• Figure 2: Power consumption breakdown under Split 8 and Split 7.1.
• Figure : EARL: Energy-Aware Adaptive Antenna Control with Reinforcement Learning