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Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach

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Abstract

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).

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Acknowledgements

This work is supported by the Beijing Municipal Natural Science Foundation (Joint Fund for Frontier Research of Fengtai Rail-Transit) under Grant L191019, the Open Project of Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education under Grant 2019FF03, the Open Project of Beijing Intelligent Logistics System Collaborative Innovation Center under Grant BILSCIC-2019KF-10, the Traffic Control Technology Innovation Fund under Grant 9907006515, the Research Base Project of Beijing Municipal Social Science Foundation under Grant 18JDGLB026 and the Science and Technique General Program of Beijing Municipal Commission of Education under Grant KM201910037003.

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Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Yunpeng Wang & Weiwei Fang

  2. Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing, 400065, China

    Weiwei Fang

  3. School of Information, Beijing Wuzi University, Beijing, 101149, China

    Yi Ding

  4. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Naixue Xiong

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  1. Yunpeng Wang
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  2. Weiwei Fang
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  4. Naixue Xiong
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Correspondence to Weiwei Fang.

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Wang, Y., Fang, W., Ding, Y. et al. Computation offloading optimization for UAV-assisted mobile edge computing: a deep deterministic policy gradient approach. Wireless Netw 27, 2991–3006 (2021). https://doi.org/10.1007/s11276-021-02632-z

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