Energy Efficiency in RSMA-Enhanced Active RIS-Aided Quantized Downlink Systems

Abstract

This work explores combining the rate-splitting multiple-access (RSMA) technique with an active reconfigurable intelligent surface (RIS) to improve the quantized multiuser multiple-input single-output network. The active RIS facilitates communication between the base station (BS) and users equipped with low-resolution quantizers, whereas RSMA improves downlink transmission efficiency. By maximizing the spectral efficiency while minimizing the power consumption at the transmitter and active RIS, we formulate an energy efficiency maximization problem by jointly designing the BS precoding matrix and active RIS reflecting matrix. The optimization problem presents nonconvexity, which makes finding the optimal solution challenging. Therefore, we reformulate the problem into a reinforcement learning-based problem that is solvable by applying deep reinforcement learning (DRL) algorithms. To ensure action accuracy, we design a constraint-matching function that integrates with the DRL algorithm, forming a DRL framework securing all problem constraints. To assess the proposed DRL algorithm, we propose an alternating-based solution that decomposes the problem into precoding matrix optimization and active reflecting matrix optimization sub-problems, which are solvable using the successive convex approximation-based method. The performance evaluations demonstrate the convergence and effectiveness of the proposed approaches in various scenarios.