Software demodulation of weak radio signals using convolutional neural network

• URL: http://arxiv.org/abs/2502.19097v1
• Date: Wed, 26 Feb 2025 12:41:25 GMT
• Title: Software demodulation of weak radio signals using convolutional neural network
• Authors: Mykola Kozlenko, Ihor Lazarovych, Valerii Tkachuk, Vira Vialkova,
• Abstract summary: We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol.<n>We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of MFSK signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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