A DRL Framework to Optimize Energy Efficiency for Quadrature Rate-Splitting Multiple Access

Maximizing energy efficiency (EE) in Multiple-Input Single-Output (MISO) downlink networks employing Quadrature Rate-Splitting Multiple Access (Q-RSMA) is a challenging task due to the non-convex optimization problem involving power allocations, beamforming vectors, and rate allocations under multiple constraints. In this paper, we propose a Deep Reinforcement Learning (DRL) framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm to maximize EE. We handle the minimum rate constraints by formulating the rate allocation as a linear programming (LP) problem, which allows for a computationally efficient solution. The beamforming vector normalization is clarified to ensure the unit norm constraints are satisfied. Simulation results demonstrate the effectiveness of the proposed approach in achieving high EE while satisfying all system constraints.