# Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications

arXiv: https://arxiv.org/abs/2502.16371 · HTML: https://mlsanity.com/paper/2502.16371/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 22 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms](https://mlsanity.com/paper/1903.11385/) (2019)

## Built on by (most cited first, 5 of 5)

- [Software demodulation of weak radio signals using convolutional neural network](https://mlsanity.com/paper/2502.19097/) (2025)
- [Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network](https://mlsanity.com/paper/2502.18233/) (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 present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.