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Supervised machine learning based signal demodulation in chaotic communications

Authors: Kozlenko, Mykola;

Supervised machine learning based signal demodulation in chaotic communications

Abstract

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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.

M. Kozlenko, "Supervised machine learning based signal demodulation in chaotic communications," 2022 International Conference on Innovative Solutions in Software Engineering (ICISSE), Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk, Ukraine, Nov. 29-30, 2022, pp. 313-317, doi: 10.5281/zenodo.7512427

Keywords

Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, deterministic chaos, chaotic signal, I.2.6, chaotic communications, bifurcation parameter keying, deep learning, convolutional neural network, demodulation, bit error rate, Machine Learning (cs.LG), I.2.6; C.2, machine learning, bifurcation, FOS: Electrical engineering, electronic engineering, information engineering, C.2, Electrical Engineering and Systems Science - Signal Processing, 68T07 (Primary) 94A12, 94A13, 94A14 (Secondary)

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