Supervised machine learning based signal demodulation in chaotic communications
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 22 references that are in the map. First posted on arXiv 27 Apr 2025.
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References · 3
- 2025Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communicationscited by 5
- 2025Software demodulation of weak radio signals using convolutional neural networkcited by 4
- 2019Artificial Intelligence Enhances the Performance of Chaos-based Wireless Communicationcited by 1
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