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

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 provid...

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Vydané v:Wireless networks Ročník 27; číslo 4; s. 2991 - 3006
Hlavní autori: Wang, Yunpeng, Fang, Weiwei, Ding, Yi, Xiong, Naixue
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.05.2021
Springer Nature B.V
Predmet:
Airspeed
Algorithms
Communications Engineering
Computation offloading
Computer Communication Networks
Convexity
Edge computing
Electrical Engineering
Energy consumption
Engineering
IT in Business
Machine learning
Mobile computing
Networks
Optimization
Original Paper
Task scheduling
Unmanned aerial vehicles
Wireless networks
Deep deterministic policy gradient
Unmanned aerial vehicle
Computation offloading
Mobile edge computing
ISSN:1022-0038, 1572-8196
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Shrnutí: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).
Bibliografia:ObjectType-Article-1
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ISSN:1022-0038
1572-8196
DOI:10.1007/s11276-021-02632-z