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Deep Learning Based Joint Beamforming Design in IRS-Assisted Secure Communications - MaRDI portal

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Deep Learning Based Joint Beamforming Design in IRS-Assisted Secure Communications

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Publication:6432017

arXiv2304.01702MaRDI QIDQ6432017

Author name not available (Why is that?)

Publication date: 4 April 2023

Abstract: In this article, physical layer security (PLS) in an intelligent reflecting surface (IRS) assisted multiple-input multiple-output multiple antenna eavesdropper (MIMOME) system is studied. In particular, we consider a practical scenario without instantaneous channel state information (CSI) of the eavesdropper and assume that the eavesdropping channel is a Rayleigh channel. To reduce the complexity of currently available IRS-assisted PLS schemes, we propose a low-complexity deep learning (DL) based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed DL-based approach can achieve a similar performance of that with conventional alternating optimization (AO) algorithms for a significant reduction in the computational complexity.




Has companion code repository: https://github.com/mayezhang/dl-irsbf-pls








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