Energy-Efficient, Fair, and QoS-Aware RL-Based Power Control for Massive MIMO Networks

Abstract

Massive MIMO networks require power-control schemes that jointly account for energy efficiency, inter-cell interference, fairness, and time-varying QoS constraints under mobility and traffic dynamics. This paper proposes a deep reinforcement learning (DRL) framework for downlink power control in a realistic multi-cell massive MIMO setting. We develop Smart-MIMO-Cell-Env, a four-cell downlink environment with 16 antennas and 6 users per cell, correlated Rayleigh channels with 3GPP-inspired large-scale fading, user mobility, time-varying traffic load, channel aging, and optional interference coordination. A centralized agent observes an eight-dimensional state (effective transmit power, aggregate array gain, average SINR and rate, inverse-SINR penalty, mobility factor, traffic load, and channel age) and emits a continuous 88-dimensional action vector of per-user powers and per-antenna gains. A composite reward balances throughput, power consumption, link quality, fairness, and temporal stability. We train and evaluate Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in Ray RLlib under identical environment, state/action spaces, and network architectures, and assess both final policies over 200 independent episodes. PPO attains a mean episode reward of −6.130, mean downlink rate 0.264, mean inverse-SINR penalty Ī = 1.714, power-efficiency indicator 1.973, mean effective power 1.37 × 10⁻¹ W, and mean array gain 1.154, whereas SAC settles at −108.724 reward with Ī = 24.032. PPO therefore learns a higher-throughput policy with substantially better link quality at a moderate power level. The results demonstrate the viability of DRL for energy-efficient, fair, and QoS-aware power control in realistic massive MIMO deployments and identify on-policy learning as the better match for the non-stationary dynamics of such networks.