In physical layer security, one interest of the community is the development of practical approaches to achieve reliable and secure communication, such as model-free approaches, in which the gradient of the channel model is required. This paper proposes a new model-free approach based on information-theoretic metrics. We train the encoder with deep reinforcement learning that uses a policy-based gradient descent algorithm whose loss function contains a feed-forward neural network. Simulation results show that our model is capable of retaining the eavesdropper’s BLER at a high level whilst ensuring the legitimate receiver’s BLER reduces to nearly zero.


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    Titel :

    Deep Reinforcement Learning For Secure Communication


    Beteiligte:


    Erscheinungsdatum :

    01.09.2022


    Format / Umfang :

    379283 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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