基于无人机辅助移动边缘计算的任务卸载
Task Offloading Based on UAV Assisted Mobile Edge Computing

由于无人机能够灵活部署，因此可以帮助提高覆盖范围和通信质量。本文考虑了一种无人机辅助的移动边缘计算系统，其中配备有计算资源的无人机可以向附近的用户设备提供卸载服务。用户将部分计算任务卸载到无人机，而其余任务在用户本地执行。我们的目标是通过联合优化用户任务调度、任务卸载比率、传输功率、无人机飞行角度和飞行速度到达最小化系统成本的目的。并且考虑到该优化问题是非凸的，我们提出了一种基于深度确定性策略梯度的强化学习计算卸载算法。通过该算法，我们可以在不可控的动态环境中获得最优的计算卸载策略。并且通过仿真结果表明，该算法优于其他强化学习算法。
Due to the flexible deployment of drones, they can help improve coverage and communication quality. This paper considers a UAV assisted mobile edge computing system, in which the UAV equipped with computing resources can provide unloading services to nearby user devices. Users offload some computing tasks to the drone, while the remaining tasks are executed locally by the user. Our goal is to minimize system costs by jointly optimizing user task scheduling, task offloading ratio, transmission power, drone flight angle, and flight speed. And considering that the optimization problem is non-convex, we propose a reinforcement learning computation offloading algorithm based on Soft Actor Critic. Through this algorithm, we can obtain the optimal computation offloading strategy in uncontrollable dynamic environments. And the simulation results show that this algorithm is superior to other reinforcement learning algorithms.