This paper considers machine learning for physical layer security design for communication in a challenging wireless environment. The radio environment is assumed to be programmable with the aid of a meta material-based intelligent reflecting surface (IRS) allowing customisable path loss, multi-path fading and interference effects. In particular, the fine-grained reflections from the IRS elements are exploited to create channel advantage for maximizing the secrecy rate at a legitimate receiver. A deep learning (DL) technique has been developed to tune the reflections of the IRS elements in real-time. Simulation results demonstrate that the DL approach yields comparable performance to the conventional approaches while significantly reducing the computational complexity.
@article{arxiv.2004.03056,
title = {Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning},
author = {Yizhuo Song and Muhammad R. A. Khandaker and Faisal Tariq and Kai-Kit Wong and Apriana Toding},
journal= {arXiv preprint arXiv:2004.03056},
year = {2021}
}