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Updated: Jan 15, 2026

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Automatic modulation classification method using fixed K-means algorithm for feature clustering processing.

Li Yuan1, Yang Chen1

  • 1Department of Mathematics and Computer Science, Hanjiang Normal University, Shiyan, China.

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Summary
This summary is machine-generated.

This study introduces an improved signal modulation classification model that significantly boosts recognition accuracy and robustness. The enhanced model reduces computational complexity, ensuring efficient real-time communication computing.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Increasing reliance on communication technology presents challenges with complex signals and noise.
  • Existing modulation classification methods suffer from low recognition accuracy.
  • Need for robust and efficient signal processing in modern communication systems.

Purpose of the Study:

  • To develop an advanced signal automatic modulation classification model.
  • To enhance recognition accuracy and robustness against noise.
  • To reduce computational complexity for real-time applications.

Main Methods:

  • Utilized a fixed K-mean algorithm for feature classification.
  • Optimized median filtering with dynamic thresholding.
  • Employed long short-term memory and data random corruption denoising to refine the autoencoder.

Main Results:

  • Achieved significant improvements in signal classification accuracy (17.6% and 16.8% higher than other models).
  • Reduced computational complexity dramatically while maintaining high accuracy.
  • Demonstrated superior communication overhead, training efficiency, and reduced memory/runtime.

Conclusions:

  • The improved model effectively increases signal recognition accuracy and robustness.
  • Significant reduction in computational complexity ensures real-time processing capabilities.
  • The model offers a practical solution for complex signal classification in communication computing.