Joint Optimization of Task Offloading and Resource Allocation in UAV-Assisted MEC Networks

Abstract

The cooperation between unmanned aerial vehicles (UAVs) and edge clouds (ECs) in mobile edge computing (MEC) networks can provide improved offloading services to the ground mobile users (MUs), especially in special situations where the number of MUs surges or the infrastructure is sparely scattered. In this work, we aim to minimize the weighted sum of delay and energy consumption among all MUs and the UAV via appropriate task offloading decision, resource allocation and location placement in the UAV-assisted MEC system. The optimization problem is formulated as a mix-integer nonconvex one, and a joint optimization algorithm based on the semidefinite relaxation (SDR) and successive convex approximation (SCA) techniques is proposed to obtain a feasible suboptimal solution. The numerical results are provided to demonstrate that our proposed joint optimization algorithm achieves lower system cost than other four baseline schemes in different scenarios.