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November 8, 2025International Journal For Multidisciplinary Research

Blind Modulation Identification Using Machine Learning And Deep Learning Algorithms

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Authors

KGKrishna Murthy GoggiMMMani Kumar Moilla

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Overview

Blind modulation identification improves modulation type recognition in wireless communication, suggesting machine learning may enhance spectrum utilization.

Key Points

  • Deep learning achieves highest performance with around 92% accuracy in modulation type recognition.
  • Machine learning methods improve classification of digital modulation schemes under varying channel conditions.
  • Automated Modulation Classification employs various classifiers, including Decision Trees and KNN, for robust identification.
  • Enhancing modulation recognition may enable better spectrum utilization in wireless communication systems.

Cite This Study

Goggi et al. (2024) studied this question.

synapsesocial.com/papers/690e8b75a5b062d7a4e739f0https://doi.org/10.36948/ijfmr.2024.v06i04.59862
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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

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  5. 5SDR implementation of a light deep learning model based CNN for joint spectrum sensing and AMC2024 · 6 citations