Software demodulation of weak radio signals using convolutional neural network

TL;DR

This paper demonstrates the use of deep convolutional neural networks for software demodulation of weak JT65A radio signals, analyzing interference immunity and error rates in noisy environments.

Contribution

It introduces a neural network-based method for demodulating weak signals in JT65A protocol and evaluates its interference immunity performance.

Findings

Interference immunity is about 1.5 dB below the theoretical limit.

The method effectively demodulates signals with SNR as low as -30 dB.

Performance metrics include symbol and bit error rates.

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.