Robust Design of RIS-aided Full-Duplex RSMA System for V2X communication: A DRL Approach

The proliferation of multiple devices and acceleration of spectral efficiency has become a pivotal requirement for the unprecedented connectivity and performance of vehicle-to-everything (V2X) networks. This paper investigates an unconventional framework of reconfigurable intelligent surface (RIS)-integrated full-duplex (FD) rate-splitting multiple access (RSMA) communication systems, which aims to maximize the spectral efficiency of uplink (UL) and downlink (DL) vehicles in V2X network. In particular, a robust spectral-efficient design for the considered RIS-integrated FD-RSMA system via joint beamforming design and power allocation at UL vehicles under imperfect channel state information is investigated. To tackle the non-convexity of the original sum-rate maximization problem, we adopt a deep reinforcement learning (DRL)-based proximal policy optimization (PPO) algorithm which leverages Markov decision process formulation. Simulation results demonstrate the effectiveness of the integration of RIS, RSMA, and FD schemes for V2X networks over half-duplex (HD) and multi-user linear precoding schemes. Furthermore, the superiority of the proposed PPO algorithm is validated over the counterpart deep deterministic policy gradient algorithm (DDPG).