mlsanityData to 14 Oct 2026

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

arXiv 2502.16371 ↗First posted 22 Feb 2025eess.SP cs.LGcited by 5

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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.

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

3 of 35 references.

References · 3

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2017An Introduction to Deep Learning for the Physical Layercited by 233
  3. 2019Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithmscited by 1

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025Software demodulation of weak radio signals using convolutional neural networkcited by 4
  2. 2025Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural networkcited by 0
  3. 2025Machine learning based animal emotion classification using audio signalscited by 0
  4. 2025Supervised machine learning based signal demodulation in chaotic communicationscited by 0
  5. 2025Research on a hybrid LSTM-CNN-Attention model for text-based web content classificationcited by 0