Computation Offloading, UAV Placement, and Resource Allocation in SAGIN

Abstract

We study the computation offloading and resource allocation optimization in space-air-ground integrated networks (SAGIN), where computation tasks are partitioned into subtasks which are executed locally at the ground users (GUs) and/or offloaded and executed at the edge servers deployed on the associated unmanned aerial vehicles (UAVs) or a cloud server, which can be reached via multihop communications over multiple low-earth-orbit (LEO) satellites. Our design aims to minimize the weighted energy consumption while satisfying the maximum delay constraints of underlying tasks. To tackle the underlying non-convex mixed integer non-linear optimization problem, we employ the alternating optimization approach where we iteratively optimize the user association, partial offloading control, computation resource and bandwidth allocation, and UAV placement until convergence. In addition, the successive convex approximation (SCA) method is employed to convexify and solve the non-convex bandwidth allocation and UAV placement sub-problems. Via numerical studies, we illustrate the effectiveness of our proposed design compared to baselines under different network settings. Specifically, our design improves about 35 – 40% in the weighted sum of energy compared to the baselines.