aiqbench analysis
This research applies convolutional neural networks to demodulate chaotic signals generated by the logistic map, achieving a low bit error rate even under significant noise conditions. The study highlights the model's ability to generalize and detect patterns outside its training set without requiring specific pattern inclusion during learning. By leveraging deep learning for deterministic chaos communication protocols, this work advances the intersection of signal processing and machine learning in secure transmission systems.
Demodulation of chaotic signals using convolutional neural network
arXiv:2607.16788v1 Announce Type: cross Abstract: Chaotic modulation is an effective communication technique that exploits deterministic chaos to produce pseudo-random signals. A widely adopted approach involves modulation of the chaotic bifurcation parameter. This paper introduces a deep learning-based demodulation method for keying of the bifurcation parameter. It describes the architecture of the convolutional neural network and evaluates performance metrics for signals generated using the chaotic logistic map. The study assesses the bit error rate for binary signals and reports a bit error rate of 0.0819 for a bifurcation parameter deviation of 1.34% under additive white Gaussian noise at a signal-to-noise ratio of -13 dB (corresponding to a normalized signal-to-noise ratio of +20 dB). The results demonstrate the capability to detect chaotic patterns even when the specific patterns were not included in the training dataset.
Abstract:Chaotic modulation is an effective communication technique that exploits deterministic chaos to produce pseudo-random signals. A widely adopted approach involves modulation of the chaotic bifurcation parameter. This paper introduces a deep learning-based demodulation method for keying of the bifurcation parameter. It describes the architecture of the convolutional neural network and evaluates performance metrics for signals generated using the chaotic logistic map. The study assesses the bit error rate for binary signals and reports a bit error rate of 0.0819 for a bifurcation parameter deviation of 1.34% under additive white Gaussian noise at a signal-to-noise ratio of -13 dB (corresponding to a normalized signal-to-noise ratio of +20 dB). The results demonstrate the capability to detect chaotic patterns even when the specific patterns were not included in the training dataset.
6 pages, 12 figures, 3 tables. This paper supersedes arXiv:2505.06243 , Published by International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, available: this https URL
Signal Processing (eess.SP); Machine Learning (cs.LG)
arXiv:2607.16788 [eess.SP]
(or arXiv:2607.16788v1 [eess.SP] for this version)
https://doi.org/10.48550/arXiv.2607.16788 arXiv-issued DOI via DataCite (pending registration)
International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, vol. 48, no. 4/W19-2025, pp. 79-84, Mar. 03, 2026
https://doi.org/10.5194/isprs-archives-XLVIII-4-W19-2025-79-2026 DOI(s) linking to related resources
Full text under the publisher's open licence · 203 words
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