Supervised machine learning based signal demodulation in chaotic communications

TL;DR

This paper introduces a machine learning approach using convolutional neural networks for demodulating signals in chaotic communication systems, achieving high accuracy even with noise.

Contribution

It presents a novel CNN-based demodulation method for chaotic bifurcation parameter modulation, demonstrating effective performance on logistic map signals.

Findings

Achieved 88% accuracy at 20 dB SNR

Effective demodulation under additive white Gaussian noise

Validated on logistic map generated signals

Abstract

A chaotic modulation scheme is an efficient wideband communication method. It utilizes the deterministic chaos to generate pseudo-random carriers. Chaotic bifurcation parameter modulation is one of the well-known and widely-used techniques. This paper presents the machine learning based demodulation approach for the bifurcation parameter keying. It presents the structure of a convolutional neural network as well as performance metrics values for signals generated with the chaotic logistic map. The paper provides an assessment of the overall accuracy for binary signals. It reports the accuracy value of 0.88 for the bifurcation parameter deviation of 1.34% in the presence of additive white Gaussian noise at the normalized signal-to-noise ratio value of 20 dB for balanced dataset.