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Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach
Wireless Networks ( IF 3 ) Pub Date : 2021-05-05 , DOI: 10.1007/s11276-021-02632-z
Yunpeng Wang , Weiwei Fang , Yi Ding , Naixue Xiong

Unmanned Aerial Vehicle (UAV) can play an important role in wireless systems as it can be deployed flexibly to help improve coverage and quality of communication. In this paper, we consider a UAV-assisted Mobile Edge Computing (MEC) system, in which a UAV equipped with computing resources can provide offloading services to nearby user equipments (UEs). The UE offloads a portion of the computing tasks to the UAV, while the remaining tasks are locally executed at this UE. Subject to constraints on discrete variables and energy consumption, we aim to minimize the maximum processing delay by jointly optimizing user scheduling, task offloading ratio, UAV flight angle and flight speed. Considering the non-convexity of this problem, the high-dimensional state space and the continuous action space, we propose a computation offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) in Reinforcement Learning (RL). With this algorithm, we can obtain the optimal computation offloading policy in an uncontrollable dynamic environment. Extensive experiments have been conducted, and the results show that the proposed DDPG-based algorithm can quickly converge to the optimum. Meanwhile, our algorithm can achieve a significant improvement in processing delay as compared with baseline algorithms, e.g., Deep Q Network (DQN).



中文翻译:

无人机辅助移动边缘计算的计算分流优化:一种深度确定性策略梯度方法

无人机(UAV)在无线系统中可以发挥重要作用,因为它可以灵活地部署以帮助改善通信的覆盖范围和质量。在本文中,我们考虑了无人机辅助的移动边缘计算(MEC)系统,其中配备有计算资源的无人机可以为附近的用户设备(UE)提供卸载服务。UE将一部分计算任务卸载到UAV,而其余任务则在该UE本地执行。受限于离散变量和能耗的限制,我们旨在通过共同优化用户调度,任务卸载率,无人机飞行角度和飞行速度来最大程度地减少最大处理延迟。考虑到这个问题的非凸性,即高维状态空间和连续作用空间,我们在强化学习(RL)中提出了一种基于深度确定性策略梯度(DDPG)的计算分流算法。利用这种算法,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。进行了广泛的实验,结果表明所提出的基于DDPG的算法可以快速收敛到最佳状态。同时,与基线算法(例如Deep Q Network(DQN))相比,我们的算法可以在处理延迟方面取得显着改善。结果表明,所提出的基于DDPG的算法可以快速收敛到最优值。同时,与基线算法(例如Deep Q Network(DQN))相比,我们的算法可以在处理延迟方面取得显着改善。结果表明,所提出的基于DDPG的算法可以快速收敛到最优值。同时,与基线算法(例如Deep Q Network(DQN))相比,我们的算法可以在处理延迟方面取得显着改善。

更新日期:2021-05-06
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