Energy-Efficient Task Offloading in UAV-RIS-Assisted Mobile Edge Computing with NOMA

Mobile Edge Computing (MEC) may face coverage and channel quality limitations due to obstructe performance-degraded communication between ground users and MEC servers. In this paper, we propose a novel MEC system. In this system, a reconfigurable intelligent surface (RIS) is utilized on a deployed unmanned aerial vehicle (UAV) to facilitate communication between ground users (GUs) and the MEC server. In addition, to further improve spectrum utilization efficiency, non-orthogonal multiple access (NOMA) technology is utilized for MEC to increase the transmission rate. Besides, the optimization of both the UAV location and RIS passive beamforming is undertaken to minimize the overall energy consumption of the system. To tackle the non-convex nature of this problem, the joint optimization problem is decomposed into two separate sub-problems to facilitate more efficient solution approaches. The complex circle manifold optimization (CCMO) algorithm and genetic algorithm (GA) are utilized to address these two sub-problems alternately. Simulation results demonstrate that the proposed scheme effectively reduces the overall energy consumption of the MEC system.