# Software demodulation of weak radio signals using convolutional neural network

arXiv: https://arxiv.org/abs/2502.19097 · HTML: https://mlsanity.com/paper/2502.19097/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 15 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 2 of 2)

- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications](https://mlsanity.com/paper/2502.16371/) (2025)

## Built on by (most cited first, 4 of 4)

- [Identity documents recognition and detection using semantic segmentation with convolutional neural network](https://mlsanity.com/paper/2503.01085/) (2025)
- [Machine learning based animal emotion classification using audio signals](https://mlsanity.com/paper/2503.18138/) (2025)
- [Supervised machine learning based signal demodulation in chaotic communications](https://mlsanity.com/paper/2505.06243/) (2025)
- [Research on a hybrid LSTM-CNN-Attention model for text-based web content classification](https://mlsanity.com/paper/2512.18475/) (2025)

## Abstract (arXiv)

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.