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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    Titel :

    Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning


    Beteiligte:
    Song, Yizhuo (Autor:in) / Khandaker, Muhammad R. A. (Autor:in) / Tariq, Faisal (Autor:in) / Wong, Kai-Kit (Autor:in) / Toding, Apriana (Autor:in)


    Erscheinungsdatum :

    2021-04-01


    Format / Umfang :

    3175386 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch