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arXiv 2004.03056eess.SPcs.LG

Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning

Heriot-Watt University(赫瑞瓦特大学);University of Glasgow(格拉斯哥大学);University College London(伦敦大学学院);Universitas Kristen Indonesia Paulus(印度尼西亚保罗克里斯蒂安大学)

Yizhuo Song, Muhammad R. A. Khandaker, Faisal Tariq, Kai-Kit Wong, Apriana Toding

英文摘要:

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.

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