Supervised machine learning based signal demodulation in chaotic communications
This work addresses signal demodulation for chaotic communication systems, presenting an incremental improvement over existing methods.
The paper tackles demodulation in chaotic communications using a convolutional neural network, achieving an accuracy of 0.88 for binary signals with specific noise and parameter conditions.
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.