Covertness-Aware Trajectory Design for UAV: A Multi-Step TD3-PER Solution

Yuanjian Li, A. Hamid Aghvami

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

9 Citations (Scopus)

Abstract

In the presence of Warden's detection, a maximization problem on transmission throughput from unmanned aerial vehicle (UAV) to legitimate nodes is considered and solved via UAV trajectory design, subject to covert, velocity and mobility constraints. With the building-distribution-based pathloss model and the Warden's uncertain location model, the formulated optimization problem is challenging to be tackled through standard offline optimization methods. Alternatively, a twin delayed deep deterministic policy gradient (TD3) approach enhanced by multi-step learning and prioritized experience replay (PER) techniques, termed as multi-step TD3-PER, is proposed to help the UAV adaptively select velocity from continuous action space. Numerical results demonstrate the effectiveness of the proposed multi-step TD3-PER solution and showcase the corresponding superiorities against provided baselines.

Original languageEnglish
Title of host publicationICC 2022 - IEEE International Conference on Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7-12
Number of pages6
ISBN (Electronic)9781538683477
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE International Conference on Communications, ICC 2022 - Seoul, Korea, Republic of
Duration: 16 May 202220 May 2022

Publication series

NameIEEE International Conference on Communications
Volume2022-May
ISSN (Print)1550-3607

Conference

Conference2022 IEEE International Conference on Communications, ICC 2022
Country/TerritoryKorea, Republic of
CitySeoul
Period16/05/2220/05/22

Keywords

  • covert communication
  • deep reinforcement learning
  • Drone
  • trajectory design

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