Hybrid Optimization for NOMA-Based Transmissive-RIS Mounted UAV Networks

In this work, we introduce a novel hybrid joint optimization framework specifically designed for enhancing the performance of consumer electronics in vehicular networks using a transmissive reconfigurable intelligent surface (T-RIS)-mounted unmanned aerial vehicle (UAV) system. The UAV employs the non-orthogonal multiple access (NOMA) protocol to broadcast data to multiple ground devices, ensuring efficient communication. Our primary objective is to maximize the overall system sum rate while adhering to key constraints such as the rate requirements of ground devices, UAV battery capacity, and UAV coordinate boundaries. The optimization challenge of maximizing the system’s sum rate is inherently non-convex and complex. To address this, we decompose the problem into manageable subproblems. The beamforming optimization problem is tackled using successive convex approximation and semi-definite programming techniques, allowing for effective handling of non-convexity. For power allocation, we employ the Lagrangian dual method along with the sub-gradient technique, ensuring optimal power distribution among devices. To optimize the UAV’s location, we propose a dueling-based double deep reinforcement learning (D3RL) framework. This approach effectively combines all computed solutions, resulting in a comprehensive joint optimization strategy. Simulation results highlight the exceptional performance of the proposed framework. Specifically, optimizing the UAV’s location leads to a substantial performance gain of up to 65.9% compared to a system where only beamforming and power allocation are optimized with the UAV positioned at the center of the service area. These findings underscore the potential of our framework in advancing consumer electronics connectivity in vehicular networks.