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Optica Advanced Photonics Congress

Neural Network Based Black-box Estimator of Mutual Information

Tobias A. Eriksson, Diego Villafani, Henrik Åhlfeldt, Laurent Schmalen

Published Jan 1, 2022• 0 citations
Publisher

Abstract

We show that neural networks with softmax outputs are capable of estimating a lower bound on the mutual information of memoryless communication channels without prior knowledge of the channel properties.
DOI
10.1364/sppcom.2022.sptu1j.3

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Physical layer security (PLS) provides lightweight security solutions in which security is achieved based on the inherent random characteristics of the wireless medium. In this paper, we consider the PLS approach called friendly jamming (FJ), which is more practical thanks to its low computational complexity. State-of-the-art methods require that legitimate users have full channel state information (CSI) of their channel. Thanks to the recent promising application of the autoencoder (AE) in communication, we propose a new FJ method for PLS using AE without prior knowledge of the CSI. The proposed AE-based FJ method can provide good secrecy performance while avoiding explicit CSI estimation. We also apply the recently proposed tool for mutual information neural estimation (MINE) to evaluate the secrecy capacity. Moreover, we leverage MINE to avoid end-to-end learning in AE-based FJ.