Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing

Wang, Liang, Wang, Kezhi, Pan, Cunhua, Xu, Wei, Aslam, Nauman and Nallanathan, Arumugam (2022) Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing. IEEE Transactions on Mobile Computing, 21 (10). pp. 3536-3550. ISSN 1536-1233

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In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, resource allocation and the trajectory of UAVs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CAT), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UAV may take off from different locations), we propose a deep Reinforcement leArning based Trajectory control algorithm (RAT). In RAT, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RAT can be adapted to any taking off points of the UAVs and can obtain the solution more rapidly than CAT once training process has been completed. Simulation results show that the proposed CAT and RAT achieve the considerable performance and both outperform traditional algorithms.

Item Type: Article
Uncontrolled Keywords: Deep Reinforcement Learning, Mobile Edge Computing, Unmanned Aerial Vehicle (UAV), Trajectory Control, User Association
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Elena Carlaw
Date Deposited: 07 Apr 2021 12:23
Last Modified: 26 Sep 2022 13:45

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