Joint Resource Allocation Optimization in Space–Air–Ground Integrated Networks

Abstract

A UAV-assisted space–air–ground integrated network (SAGIN) can provide communication services for remote areas and disaster-stricken regions. However, the increasing types and numbers of ground terminals (GTs) have led to the explosive growth of communication data volume, which is far from meeting the communication needs of ground users. We propose a mobile edge network model that consists of three tiers: satellites, UAVs, and GTs. In this model, UAVs and satellites deploy edge servers to deliver services to GTs. GTs with limited computing capabilities can upload computation tasks to UAVs or satellites for processing. Specifically, we optimize association control, bandwidth allocation, computation task allocation, caching decisions, and the UAV’s position to minimize task latency. However, the proposed joint optimization problem is complex, and it is difficult to solve. Hence, we utilize Block Coordinate Descent (BCD) and introduce auxiliary variables to decompose the original problem into different subproblems. These subproblems are then solved using the McCormick envelope theory, the Successive Convex Approximation (SCA) method, and convex optimization techniques. The simulation results extensively illustrate that the proposed solution dramatically decreases the overall latency when compared with alternative benchmark schemes.