Wireless-Powered UAV-Assisted MEC: Joint Resource Allocation and Trajectory Optimization

Abstract

With their versatility and flexibility, unmanned aerial vehicles (UAVs) have enormous potential to enhance communication and computation services for terminal devices (TDs) in the Internet of Things. In this paper, we present a wireless-powered UAV-assisted mobile edge computing system, where the UAV is dispatched to assist the TDs to execute their computation-intensive tasks. To ensure the sustainability of the system, the access point leverages wireless power transfer to energize the UAV, which broadcasts a portion of the harvested energy to all the TDs, while utilizing the remaining energy for its own operations. We aim to minimize total system energy consumption over the entire execution period, by jointly optimizing resource allocation and UAV trajectory. However, the formulated optimization problem is non-convex and difficult to solve directly. To address this issue, we propose an iterative algorithm based on successive convex approximation, which iteratively optimizes CPU frequency, task allocation, and UAV trajectory variables until convergence. The numerical results demonstrate that the proposed scheme achieves significant energy saving compared to the baselines.