Rate Maximization for Double-RIS Assisted Downlink NOMA Under Statistical CSI

This letter investigates a double-reconfigurable intelligent surface (RIS) assisted non-orthogonal multiple access (NOMA) system. The sum-rate maximization problem is formulated by jointly optimizing the power allocation at the base station and the cooperative passive beamforming at the two distributed RISs. To tackle the highly coupled non-convex optimization problem, we develop a low complexity alternating optimization framework by decomposing the original problem into two tractable subproblems, where the subproblems are solved based on geometric programming and fractional programming, respectively. Both instantaneous and statistical channel state information (CSI) scenarios are considered in the optimization. Simulation results demonstrate that the proposed design achieves significant sum-rate gains compared to the existing benchmark schemes.