Hybrid UAV-Enabled Secure Offloading via Deep Reinforcement Learning

被引：7
|
作者：
Yoo, Seonghoon [1 ]
Jeong, Seongah [2 ]
Kang, Joonhyuk [1 ]
机构：
[1] Korea Adv Inst Sci & Technol, Dept Elect Engn, Daejeon 34141, South Korea
[2] Kyungpook Natl Univ, Sch Elect & Elect Engn, Daegu 14566, South Korea
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2023年 / 12卷 / 06期
关键词：
Autonomous aerial vehicles; Jamming; Relays; Wireless communication; Servers; Switches; Reinforcement learning; Unmanned aerial vehicle (UAV); offloading; physical-layer security; deep reinforcement learning; RESOURCE-ALLOCATION;
D O I：
10.1109/LWC.2023.3254554
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this letter, we consider a secure offloading system consisting of a unmanned aerial vehicle (UAV)-mounted edge server, ground user equipments (UEs) and a malicious eavesdropper UAV. With the aim of maximizing secrecy sum-rate, we propose an adaptation of a helper UAV to switch the mode between jamming and relaying. We jointly optimize the helper UAV's trajectory and mode and UEs' offloading decision under energy budget constraints and operational limitations of nodes. The proposed algorithm is developed based on a deep deterministic policy gradient (DDPG)-based method, whose superior performances are verified via numerical results, as compared to other benchmark schemes.
引用
收藏
页码：972 / 976
页数：5
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