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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    Title :

    Truly Intelligent Reflecting Surface-Aided Secure Communication Using Deep Learning


    Contributors:


    Publication date :

    2021-04-01


    Size :

    3175386 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English