Deep Learning-based Codes for Wiretap Fading Channels

• URL: http://arxiv.org/abs/2409.08786v1
• Date: Fri, 13 Sep 2024 12:45:30 GMT
• Title: Deep Learning-based Codes for Wiretap Fading Channels
• Authors: Daniel Seifert, Onur Günlü, Rafael F. Schaefer,
• Abstract summary: This work provides the first experimental characterization of a deep learning-based, finite-blocklength code construction for multi-tap fading wiretap channels. In addition to the evaluation of the average probability of error and information leakage, we illustrate the influence of (i) the number of fading taps, (ii) differing variances of the fading coefficients and (iii) the seed selection for the hash function-based security layer.
• Score: 27.397083701319744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The wiretap channel is a well-studied problem in the physical layer security (PLS) literature. Although it is proven that the decoding error probability and information leakage can be made arbitrarily small in the asymptotic regime, further research on finite-blocklength codes is required on the path towards practical, secure communications systems. This work provides the first experimental characterization of a deep learning-based, finite-blocklength code construction for multi-tap fading wiretap channels without channel state information (CSI). In addition to the evaluation of the average probability of error and information leakage, we illustrate the influence of (i) the number of fading taps, (ii) differing variances of the fading coefficients and (iii) the seed selection for the hash function-based security layer.

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