Citation

Ahmed, Manzoor and Hussain, Touseef and Shahwar, Muhammad and Khan, Feroz and Sheraz, Muhammad and Khan, Wali Ullah and Chuah, Teong Chee and Lee, It Ee (2025) Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement learning. PeerJ Computer Science, 11. e2902. ISSN 2376-5992

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Abstract

This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Jun 2025 04:52
Last Modified:	30 Jun 2025 04:52
URII:	http://shdl.mmu.edu.my/id/eprint/14167

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