Demodulation of chaotic signals using convolutional neural network

2026-07-18

AI Summary

This study addresses the challenge of efficiently demodulating chaotic bifurcation parameter shift keying signals under low signal-to-noise ratio (SNR) conditions. To this end, the authors propose an end-to-end demodulation method based on convolutional neural networks that directly recovers binary information from chaotic time series generated by the logistic map, without relying on conventional synchronization mechanisms. This work represents the first application of deep learning to the demodulation of such chaotic signals and demonstrates a notable ability to recognize chaotic patterns not encountered during training. Evaluated in additive white Gaussian noise, the method achieves a bit error rate of 0.0819 even at an SNR as low as −13 dB (corresponding to a normalized SNR of +20 dB) with a 1.34% deviation in the bifurcation parameter, thereby confirming its robustness and generalization capability.

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.

Problem

• chaotic signals
• demodulation
• bifurcation parameter
• bit error rate
• logistic map

Innovation

• chaotic modulation
• convolutional neural network
• bifurcation parameter keying
• deep learning demodulation
• logistic map