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Artificial Bee Colony Based Gabor Parameters Optimizer (ABC-GPO) for Modulation Classification.

Saad AlJubayrin1, Mubashar Sarfraz2, Sajjad A Ghauri3

  • 1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia.

Computational Intelligence and Neuroscience
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Summary
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This study introduces an advanced modulation classification method for cognitive radio using a Gabor filter network and an artificial bee colony algorithm. The proposed technique enhances signal identification accuracy in unknown environments.

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Area of Science:

  • Signal Processing
  • Cognitive Radio Technology
  • Machine Learning for Communications

Background:

  • Modulation classification is crucial for cognitive radio systems operating with unknown signal information.
  • Accurate modulation identification enables efficient spectrum sensing and dynamic spectrum access.

Purpose of the Study:

  • To develop and evaluate a novel pattern recognition-based modulation classification approach.
  • To improve the accuracy and robustness of modulation identification in cognitive radio applications.

Main Methods:

  • A two-module approach involving parameter extraction and feature selection using a Gabor filter network (GFN).
  • Utilized Delta rule and Recursive Least Square (RLS) algorithm for adaptive filter tuning.
  • Optimized Gabor parameters and classifier performance with the Artificial Bee Colony (ABC) algorithm.

Main Results:

  • The proposed GFN-based classifier demonstrated superior performance across various modulation schemes (BPSK, QPSK, 8PSK, 16PSK, 64PSK, 4FSK, 8FSK, 16FSK, QAM, 8QAM, 16QAM, 32QAM, 64QAM).
  • Simulation results confirmed the effectiveness of the ABC algorithm in optimizing classifier parameters.
  • The method achieved high accuracy in classifying signals even with unknown prior information.

Conclusions:

  • The developed modulation classification system offers a significant improvement over existing methods.
  • The integration of GFN, adaptive filtering, and ABC optimization provides a robust solution for cognitive radio.
  • This approach enhances the adaptability and intelligence of cognitive radio systems.