Task Offloading and Energy Optimization in Hybrid UAV-Assisted Mobile Edge Computing Systems

Abstract

The paper considers a more challenging task offloading scenario in hybrid UAV-assisted mobile edge computing (MEC) systems, where multiple dual-function UAVs tour in the sky to serve ground users (GUs) by acting as edge servers or aerial relays. Since each task can be executed on GUs, UAVs and the base station (BS) in parallel, the service assignment, task splitting, trajectory of UAVs, as well as resource and transmission power of both UAVs and GUs should be jointly optimized to minimize the system energy consumption with the subjection of the maximum tolerable latency and computing limitations. To tackle such mixed integer non-linear programming (MINLP) problem, a deep reinforcement learning (DRL) combined successive convex approximation (SCA) algorithm is proposed in the paper to seek a close optimal solution with low-complexity. In specific, the binary service assignment and continuous task splitting are obtained by DRL, while the trajectory planning and resource scheduling are jointly optimized by SCA in sequence to speed up the convergence. Numerical results demonstrate that the proposed DRL-SCA algorithm equipped with dual-function UAV scheme is more effective in making full use of the on-board resource of UAVs and reducing the overall system energy consumption.