Offloading Optimization in Digital Twin-Aided UAV Networks

被引：0
|
作者：
Miao J. [1 ]
Zheng H. [1 ]
Xie Z. [1 ]
Lai J. [1 ]
Jiang L. [1 ]
机构：
[1] Guangdong Key Laboratory of Internet of Things Information Technology, Guangdong University of Technology, Guangzhou
来源：
Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2022年 / 45卷 / 06期
关键词：
Computing tasks offloading; Digital twin; Proximal policy optimization; Unmanned aerial vehicle networks; Virtual-real mapping error;
D O I：
10.13190/j.jbupt.2022-181
中图分类号：
学科分类号：
摘要：
To address the problemthat limited resource cannot meet the computing requirements of resource-intensive tasks in dynamic and time-varying unmanned aerial vehicle (UAV) networks, a digital twin technology is leveraged to construct the twin model of UAV networks, and a scheme of computing tasks offloading is developed for smart terminal. Then, the problem of computing offloading is modeled as a Markov decision process, and an optimization model is established for jointly optimizing UAV hovering point selection, computing tasks offloading decision, and UAV computing resource allocation. Considering the virtual-real mapping error between the twin model and the real UAV networks, a computing tasks offloading approach is designed based on proximal strategy optimization. Numerical results illustrate that the proposed approach can better adapt to the virtual-real mapping error comparing with the traditional deep reinforcement learning. © 2022, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.
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页码：133 / 139
页数：6
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