Supervised machine learning based signal demodulation in chaotic communications
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912368526622720 |
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| author | Kozlenko, Mykola |
| author_facet | Kozlenko, Mykola |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06243 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Supervised machine learning based signal demodulation in chaotic communications Kozlenko, Mykola Signal Processing Machine Learning 68T07 (Primary) 94A12, 94A13, 94A14 (Secondary) I.2.6; C.2 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. |
| title | Supervised machine learning based signal demodulation in chaotic communications |
| topic | Signal Processing Machine Learning 68T07 (Primary) 94A12, 94A13, 94A14 (Secondary) I.2.6; C.2 |
| url | https://arxiv.org/abs/2505.06243 |