[en] 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.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Ahmed, Manzoor; Artificial Intelligence Industrial Technology, Research Institute, Hubei Engineering University, Xiaogan City, China ; Hubei Engineering University, Xiaogan City, China
Hussain, Touseef ; School of Electronic Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
Shahwar, Muhammad ; School of Computer Science and Technology, Qingdao University, Qingdao, China
Khan, Feroz ; School of Electronic Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
Sheraz, Muhammad; Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, Selangor, Malaysia
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Chuah, Teong Chee; Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, Selangor, Malaysia
Lee, It Ee; Centre for Smart Systems and Automation, CoE for Robotics and Sensing Technologies, Faculty of Artificial Intelligence and Engineering, Multimedia University, Persiaran Multimedia, Cyberjaya, Selangor, Malaysia
External co-authors :
yes
Language :
English
Title :
Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement learning.
Multimedia University Research Fellow Grant TM Research and Development Grant The Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation Natural Science Foundation of Xiaogan City Science and Technology Research Project of Education Department of Hubei Province
Funding text :
The following grant information was disclosed by the authors: Multimedia University Research Fellow Grant: MMUI/240021. TM Research and Development Grant: RDTC/241149. The Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation: T201410, T2020017. Natural Science Foundation of Xiaogan City: XGKJ2022010095, XGKJ2022010094. Science and Technology Research Project of Education Department of Hubei Province: Q20222704.This work was supported by the Multimedia University Research Fellow Grant (MMUI/ 240021) and the TM Research and Development Grant (RDTC/241149). The Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (T201410, T2020017), the Natural Science Foundation of Xiaogan City (XGKJ2022010095, XGKJ2022010094), the Science and Technology Research Project of Education Department of Hubei Province (No. Q20222704). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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