智能反射面赋能无人机边缘网络计算卸载方案

In order to address the challenge of low offloading rate caused by the obstacles blocking in the links between unmanned aerial vehicle (UAV) and ground users (GU) in urban scene, a partial task offloading scheme for UAV-enabled mobile edge computing with the aid of reconfigurable intelligence surface was proposed. A nonconvex and multivariable coupling stochastic optimization problem was formulated by the joint design of the computation task allocation, the transmit power of GU, the phase shift of RIS, UAV computation resource, and UAV trajectory, aiming at maximizing the minimum average data throughput of GU. By leveraging the properties of mathematical expectation, the stochastic optimization problem was transformed into a deterministic optimization problem. Then, the deterministic optimization problem was decomposed into three subproblems by using the block coordinate descent (BCD) algorithm. By introducing auxiliary variables, the nonconvex problems were transformed into convex optimization problems via the successive convex approximation and semidefinite relaxation, and then the approximate suboptimal solution of the original problem was obtained. The simulation results show that the proposed algorithm has good convergence performance and effectively improves the average data throughput of GU.