Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

Wang, Liang, Wang, Kezhi, Pan, Cunhua, Xu, Wei, Aslam, Nauman and Hanzo, Lajos (2021) Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing. IEEE Transactions on Cognitive Communications and Networking, 7 (1). pp. 73-84. ISSN 2372-2045

[img]
Preview
Text
2009.11277.pdf - Accepted Version

Download (410kB) | Preview
Official URL: https://doi.org/10.1109/TCCN.2020.3027695

Abstract

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV’ UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs’ trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

Item Type: Article
Uncontrolled Keywords: Multi-Agent Deep Reinforcement Learning, MADDPG, Mobile Edge Computing, UAV, Trajectory Control
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 01 Dec 2020 11:58
Last Modified: 09 Mar 2021 15:00
URI: http://nrl.northumbria.ac.uk/id/eprint/44878

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics