Browsing by Subject "Mutual information neural estimation"

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    On secure communications over gaussian wiretap channels via finite-length codes
    (IEEE, 2020) Nooraiepour, A.; Aghdam, S. R.; Duman, Tolga M.
    Practical codes for the Gaussian wiretap channel are designed aiming at satisfying information-theoretic metrics to ensure security against a passive eavesdropper (Eve). Specifically, a design criterion is introduced for the coset coding scheme in order to satisfy a strong secrecy condition described with the mutual information between the secret message and Eve's observation. In addition, mutual information neural estimation (MINE) powered from deep learning tools is applied in order to directly compute the information-theoretic security constraint, and verify the proposed solutions. It is shown that finite-length coset codes can indeed ensure secure transmission from an information-theoretic perspective.