Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surface

Conference presentation

Dagiuklas, A. (2023). Deep Reinforcement Learning for Secrecy Energy-Efficient UAV Communication with Reconfigurable Intelligent Surface. IEEE WCNC. Scotland 26 - 29 Mar 2023 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/wcnc55385.2023.10118891
AuthorsDagiuklas, A.
TypeConference presentation
Abstract

This paper investigates the physical layer security (PLS) issue in reconfigurable intelligent surface (RIS) aided millimeter-wave rotary-wing unmanned aerial vehicle (UAV) communications under the presence of multiple eavesdroppers and imperfect channel state information (CSI). The goal is to maximize the worst-case secrecy energy efficiency (SEE) of UAV via a joint optimization of flight trajectory, UAV active beamforming and RIS passive beamforming. By interacting with the dynamically changing UAV environment, real-time decision making per time slot is possible via deep reinforcement learning (DRL). To decouple the continuous optimization variables, we introduce a twin-twin-delayed deep deterministic policy gradient (TTD3) to maximize the expected cumulative reward, which is linked to SEE enhancement. Simulation results confirm that the proposed method achieves greater secrecy energy savings than the traditional twin-deep deterministic policy gradient DRL (TDDRL)-based method.

KeywordsSecrecy energy efficiency, deep reinforcement learning, physical layer security, reconfigurable intelligent surface, unmanned aerial vehicle
Year2023
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Digital Object Identifier (DOI)https://doi.org/10.1109/wcnc55385.2023.10118891
Web address (URL)https://wcnc2023.ieee-wcnc.org/
Accepted author manuscript
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Print26 Mar 2023
Publication process dates
Deposited08 Feb 2023
Accepted08 Jan 2023
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