Abstract: As a new architecture,the space-air-ground integrated communication technology can effectively improve the network service quality of ground terminal,and has attracted widespread attention in recent years.This paper studies a space-air-ground integrated mobile edge computing system,in which multiple UAVs provide low-latency edge computing services for ground devices,and low earth orbit satellites provide ubiquitous cloud computing services for ground devices.Since the deployment position of the UAVs and the scheduling scheme of computing tasks are the key factors affecting the performance of the system,the deployment position of the UAVs,the link relationship between the ground terminal and the UAVs,and the offloading ratio of computing tasks need to be jointly optimized to minimize the average task response delay of the system.Since the formally defined joint optimization problem is a mixed nonlinear programming problem,this paper designs a two-layer optimization algorithm.In the upper layer of the algorithm,a particle swarm optimization algorithm that combines genetic algorithm operators is proposed to optimize the deployment position of the UAVs,and the greedy algorithm is used in the lower layer of the algorithm to optimize the computing task offloading scheme.The extensive simulation experiments verify the feasibility and effectiveness of the proposed method.The results show that the proposed method can achieve lower average task response time,compared to other baseline methods.

Key words: Space-Air-Ground integrated network, Mobile edge computing, Unmanned aerial vehicle deployment, Computation offloading