Software demodulation of weak radio signals using convolutional neural network
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917936930750464 |
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| author | Kozlenko, Mykola Lazarovych, Ihor Tkachuk, Valerii Vialkova, Vira |
| author_facet | Kozlenko, Mykola Lazarovych, Ihor Tkachuk, Valerii Vialkova, Vira |
| contents | In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_19097 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Software demodulation of weak radio signals using convolutional neural network Kozlenko, Mykola Lazarovych, Ihor Tkachuk, Valerii Vialkova, Vira Machine Learning Signal Processing 68T07 (Primary) 94A14, 68M10 (Secondary) In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals. |
| title | Software demodulation of weak radio signals using convolutional neural network |
| topic | Machine Learning Signal Processing 68T07 (Primary) 94A14, 68M10 (Secondary) |
| url | https://arxiv.org/abs/2502.19097 |