Joint Resource Allocation and Trajectory Design for UAV-assisted Mobile Edge Computing Systems

Abstract

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is an appealing concept, where a fixed-wing UAV equipped with computing resources is used to help local resource-limited user devices (UDs) compute their tasks. In this paper, each UD has separable computing tasks to complete, which can be divided into two parts: one portion is processed locally and the other part is offloaded to the UAV. The UAV moves around above UDs and provides computing service in an orthogonal frequency division multiple access (OFDMA) manner. This paper aims to minimize the weighted sum energy consumption of the UAV and UDs by jointly optimizing resource allocation and UAV trajectory. The resulted optimization problem is nonconvex and challenging to solve directly. With that in mind, we develop an iterative algorithm for solving this problem based on the block coordinate descent method, which iteratively optimizes resource allocation variables and UAV trajectory variables till convergence. Simulation results show significant energy saving of our proposed solution compared to the benchmarks.