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Spectrum Sensing Implemented with Improved Fluctuation-Based Dispersion Entropy and Machine Learning.

Gianmarco Baldini1, Jean-Marc Chareau1, Fausto Bonavitacola2

  • 1European Commission, Joint Research Centre, 21027 Ispra, Italy.

Entropy (Basel, Switzerland)
|December 24, 2021
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Summary
This summary is machine-generated.

This study introduces an advanced machine learning method for spectrum sensing, utilizing an improved Fluctuation Dispersion Entropy (FDE) feature. The novel approach enhances detection accuracy for cognitive radio networks, outperforming existing methods in various signal conditions.

Keywords:
entropymachine learningsignal processingspectrum sensing

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Spectrum sensing is crucial for efficient radio frequency spectrum management and cognitive radio networks.
  • Secondary users must detect primary users to avoid interference and ensure proper spectrum utilization.

Purpose of the Study:

  • To propose a novel machine learning implementation for spectrum sensing using entropy measures.
  • To enhance spectrum sensing robustness and accuracy, particularly in noisy environments.

Main Methods:

  • Utilized Fluctuation Dispersion Entropy (FDE) as a feature vector for machine learning models.
  • Developed an improved FDE (IFDE) and an adaptive IFDE (AIFDE) for noise robustness and automatic hyper-parameter selection.
  • Combined machine learning with entropy measures for spectrum sensing classification.

Main Results:

  • The proposed AIFDE-based machine learning approach demonstrated superior performance compared to other entropy measures and the Energy Detector (ED).
  • Consistent outperformance was observed across various Signal-to-Noise Ratio (SNR) levels and fading conditions.
  • The method effectively classifies the presence or absence of primary users.

Conclusions:

  • The novel AIFDE machine learning approach offers a robust and accurate solution for spectrum sensing in cognitive radio.
  • This method represents a significant advancement in spectrum sensing techniques, improving coexistence and spectrum efficiency.