Hybrid UAV-Enabled Secure Offloading via Deep Reinforcement Learning

Seonghoon Yoo, Seongah Jeong, Joonhyuk Kang

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)972-976
Number of pages5
JournalIEEE Wireless Communications Letters
Volume12
Issue number6
DOIs
StatePublished - 1 Jun 2023

Keywords

  • deep reinforcement learning
  • offloading
  • physical-layer security
  • Unmanned aerial vehicle (UAV)

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