Secure Resource Allocation and Trajectory Design for RIS and NOMA Assisted Multi-UAV Systems

Uncrewed aerial vehicle (UAV) assisted nonorthogonal multiple access (NOMA) communications show great potential in providing simultaneous service to various users. This article proposes a novel reconfigurable intelligent surface (RIS) aided UAV-swarm NOMA system, where the Coordinated Multipoint (CoMP) technique is employed to manage intercell interference for all users. The primary objective is to maximize the overall security rate by jointly optimizing the UAV swarm trajectories, power distribution among the UAVs, and the reflection coefficients of the RIS. To address the nonconvex nature and interdependence of this optimization problem while accommodating long-term dynamic requirements, we introduce an optimization framework that combines alternating optimization (AO) with the weighted-QMIX algorithm. Specifically, successive convex optimization (SCA) is utilized within the AO method to determine the optimal power allocation and RIS coefficients. Meanwhile, the weighted-QMIX algorithm conducts online trajectory optimization for all UAVs through a multiagent deep reinforcement learning (DRL) process. Numerical results demonstrate that: 1) the proposed method achieves a higher communication security rate compared to systems lacking RIS, NOMA, or stationary UAVs and 2) the proposed algorithm outperforms a pure DRL algorithm in terms of both computational complexity and security performance.