Distributed RIS-Assisted FD Systems with Discrete Phase Shifts: A Reinforcement Learning Approach

This paper studies the sum-rate maximization problem of a distributed reconfigurable intelligent surface (RIS)-assisted full-duplex wireless system, where the availability of finite-resolution phase shifts at the RIS is considered. The aim is to optimize the transmit beamformers and RIS phase shifts, subject to the practical discrete phase shift and power constraints. The optimization problem is decoupled into two sub-problems; transmit beamforming and RIS phase shifts optimization. The transmit beamforming problem is mathematically addressed using approximate and closed-form solutions, while the discrete RIS phase shifts are optimized using a reinforcement learning (RL) approach. The existence and absence of a strong direct line-of-sight is investigated to show the effect of the phase shift optimization on the sum-rate. Simulation results illustrate that the proposed RL for the discrete phase shifts optimization provides a near-optimal performance with a small number of bits even for a large number of RIS elements, while improving the sum-rate compared to the random phase shift scenario and reducing the computational complexity compared to the state-of-the-art works.