Federated Learning Empowered Mobile RISs for NOMA Networks

Abstract

A novel framework of reconfigurable intelligent surfaces (RISs) enhanced indoor wireless networks is proposed, where an RIS mounted on the robot is invoked to enhance the service quality for mobile users. Meanwhile, non-orthogonal multiple access (NOMA) techniques are adopted to further increase the spectrum efficiency since RISs are capable to provide NOMA with artificially controlled channels, which can be a beneficial condition for NOMA networks. To optimize the sum rate of all users, a federated learning enhanced deep deterministic policy gradient (FL-DDPG) algorithm is proposed to optimize the deployment and phase shifts of the mobile RIS as well as the power allocation policy. Our simulation results indicate that the mobile RIS scheme can provide about three times data rate gain compare to the fixed RIS. Moreover, the NOMA scheme is capable to achieve a significant data rate gain in contrast with the OMA scheme. Finally, the FL-DDPG algorithm has a superior convergence rate and optimization performance than that of the independent training framework.