Deep Reinforcement Learning for RIS-Aided Non-Orthogonal Multiple Access Downlink Networks

Abstract

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves the optimization of phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. For intelligently adjusting the phase shifting matrix of the access point (AP), we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Extensive simulation results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves better sum data rate compared with orthogonal multiple access (OMA) networks. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy, while conventional optimization approaches can not. 3) Compared with increasing the transmit power of the AP, increasing the number of reflecting elements (REs) is a more efficiency method to improve the sum data rate.