Joint Trajectory Optimization and Task Offloading for UAV-Assisted Mobile Edge Computing

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has been touted as a promising solution for providing computing services in disaster relief and other settings due to its flexibility and ease of deployment. Nevertheless, providing computing services for a large number of mobile devices is challenged by UAVs' limited computation and energy resources. To this end, we propose a scheme for joint trajectory optimization and task offloading that aims to minimize the total delay of all computing tasks. Our proposed scheme involves formulating the scheduling of mobile devices and computing tasks, adjustment of UAV flight angle and speed, and transmission power control as a non-convex mixed integer programming problem. In order to address the issue, we establish a Markov decision process (MDP) for UAV-assisted MEC systems. Given the high-dimensional continuous action space, we adopt a reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG). The results of the simulation indicate that our suggested scheme outperforms the baseline schemes in processing delay, and the DDPG-based algorithm exhibits rapid convergence.