CSI Learning Based Active Secure Coding Scheme For Detectable Wiretap Channel

TL;DR

This paper introduces an active secure coding scheme that leverages machine learning to adaptively learn channel state information in a detectable wiretap channel, enhancing security and reliability.

Contribution

It proposes a novel combination of machine learning algorithms with physical layer secure coding to improve security in channels with uncertain CSI.

Findings

Achieves reliable and secure communication with learned CSI

Effectively models the channel with a hidden Markov process

Demonstrates improved security performance through simulations

Abstract

In this paper, we consider the problem of secure and reliable communication with uncertain channel state information (CSI) and present a new solution named active secure coding which combines the machine learning methods with the traditional physical layer secure coding scheme. First, we build a detectable wiretap channel model by combining the hidden Markov model with the compound wiretap channel model, in which the varying of channel block CSI is a Markov process and the detected information is a stochastic emission from the current CSI. Next, we present a CSI learning scheme to learn the CSI from the detected information by the Baum-Welch and Viterbi algorithms. Then we construct explicit secure polar codes based on the learned CSI, and combine it with the CSI learning scheme to form the active secure polar coding scheme. Simulation results show that an acceptable level of reliability and security can be achieved by the proposed active secure polar coding scheme.