Capacity Enhancement of UAV-Assisted RIS-NOMA Network

In this paper, we propose a novel reconfigurable intelligent surface (RIS) -assisted non-orthogonal multiple access (NOMA) model, where an unmanned aerial vehicle (UAV) is employed for energy transfer. In this framework, the ground users (GUs) utilize energy from the UAV for information transmission to enable long-duration communication self-sufficiency. A sum-rate maximization problem is proposed, which is decomposed into four sub-problems: phase-shift optimization, power allocation, time allocation, and UAV trajectory optimization. These are solved using the SDR algorithm, CVX toolbox, game theory, and PSO algorithm, respectively. Subsequently, a joint resource allocation algorithm based on the block coordinate descent (BCD) method is introduced. Simulation results show that the UAV-assisted RIS-NOMA scheme, along with the proposed BCD algorithm, can increase the system capacity by 48.6% compared to the TDMA scheme as well as the alternating direction multiplier method (ADMM) and whale optimization algorithm (WOA).