Software demodulation of weak radio signals using convolutional neural network
This work addresses weak signal demodulation for wide-area monitoring in electric power systems, representing an incremental improvement.
The paper tackled software demodulation of weak JT65A radio signals using a convolutional neural network, achieving interference immunity within 1.5 dB of the theoretical limit for non-coherent demodulation of orthogonal MFSK signals.
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.