Optimizing uplink power control for energy efficiency in mmWave user-centric cell-free massive MIMO with deep reinforcement learning

Abstract User-centric (UC) Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) millimeter-wave (mmWave) networks are a promising solution to meet the performance requirements of next-generation wireless systems. However, maximizing energy efficiency in dense deployments remains challenging due to coordination overhead and highly dynamic propagation conditions. This work addresses uplink power control in UC CF-mMIMO networks and proposes a Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach trained under a centralized training and decentralized execution (CTDE) paradigm. The simulations are performed in a PyTorch library and rely on 3GPP TR 38.901 specification for the mmWave channel model over a UC architecture with 35 user equipments (UEs) and 100 distributed access points (APs). Simulation results indicate clear gains over both DRL baselines and conventional optimization methods. In particular, the proposed scheme reaches an energy efficiency of up to 380 Mbit/joule and maintains spectral efficiencies above 18 bps/Hz. Moreover, the method also preserves user-level reliability with a median minimum per-user spectral efficiency remains above 9 bps/Hz, and the Jain fairness index reaches 0.96, preventing resource starvation while maintaining strict QoS guarantees. These findings demonstrate that multi-agent cooperation enables robust and energy-efficient power control policies, paving the way for cost-effective and scalable UC CF-mMIMO deployments.