Power Inspection and Unloading Strategy of UAV Based on Game Theory and Reinforcement Learning

被引：0

作者：

Deng F. ^{[1
]}

Shan Y. ^{[1
]}

Xie Z. ^{[1
]}

Zhang P. ^{[2
]}

He Y. ^{[3
]}

机构：

[1] School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang

[2] School of Electrical Engineering, Beijing Jiaotong University, Haidian District, Beijing

[3] School of Electrical Engineering, Wuhan University, Wuhan

来源：

Dianwang Jishu/Power System Technology | 2021年 / 45卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Deep learning; Edge servers; Game theory; Offloading; UAVs;

D O I：

10.13335/j.1000-3673.pst.2020.1753

中图分类号：

学科分类号：

摘要：

Due to the limitation of the equipment computing capacity and the energy resources, UAVs cannot perform intensive computing tasks well. In order to solve this problem, this paper proposes an unloading strategy based on the game theory and the deep learning. This strategy establishes the cooperative games between the unmanned aerial vehicles (UAV), so the minimized cost function is defined as the energy costs and delays, and the existence of at least one Nash equilibrium (NE) is proved. A distributed algorithm for solving the NE of both the game sides is proposed. On this basis, through the reinforcement learning method based on the theory of SLA, the UAVs effectively realizes the implementation of the edge server option. Simulation results show that compared with the other unloading strategies, the unloading mechanism proposed in this paper has a significant effect on reducing the UAV energy consumption, the system costs and the network delays. © 2021, Power System Technology Press. All right reserved.

引用

页码：3649 / 3657

页数：8

共 32 条

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