期刊
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
卷 7, 期 1, 页码 73-84出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCCN.2020.3027695
关键词
Multi-agent deep reinforcement learning; MADDPG; mobile edge computing; UAV; trajectory control
资金
- NSFC [62022026, 61871109]
- Engineering and Physical Sciences Research Council [EP/N004558/1, EP/P034284/1, EP/P003990/1]
- Royal Society's Global Challenges Research Fund
- EEuropean Research Council's Advanced Fellow Grant QuantCom
- EPSRC [EP/N004558/1, EP/P034284/1] Funding Source: UKRI
A UAV-aided mobile edge computing framework is proposed to optimize fairness and energy consumption. A multi-agent deep reinforcement learning algorithm is used to manage UAV trajectories, with a low-complexity approach for optimizing offloading decisions of UEs. The proposed solution shows considerable performance improvements compared to traditional algorithms.
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.
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