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September 10, 2025SensorsOpen Access

Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks

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Authors

HCHaithem Ben ChikhaAAAlaa AlaerjanRJRanda Jabeur

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Overview

The study implements a deep residual network to classify 5G modulation, highlighting improved accuracy in complex waveforms.

Key Points

  • The proposed algorithm significantly improves classification accuracy for 5G modulation signals.
  • Performance metrics indicate enhanced recall, precision, and F-measure compared to traditional methods.
  • Deep learning and PCA are integrated for effective feature extraction and dimensionality reduction.
  • This first application of deep learning addresses complex modulation types in future wireless systems.

Cite This Study

Chikha et al. (2025) studied this question.

synapsesocial.com/papers/68c1ad6a54b1d3bfb60e5df3https://doi.org/10.3390/s25154682
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