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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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Machine Learning for LTE Energy Detection Performance Improvement.

Małgorzata Wasilewska1, Hanna Bogucka2

  • 1Department of Wireless Communications, Poznan University of Technology, 61-131 Poznan, Poland. Malgorzata.Wasilewska@doctorate.put.poznan.pl.

Sensors (Basel, Switzerland)
|October 11, 2019
PubMed
Summary
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Machine learning algorithms enhance spectrum sensing for dynamic spectrum access. By analyzing radio signals, these techniques improve the efficiency of wireless communication systems, enabling better spectrum sharing.

Area of Science:

  • Electrical Engineering
  • Computer Science
  • Wireless Communications

Background:

  • Increasing demand for radio devices and limited spectrum necessitate dynamic spectrum access.
  • Cognitive radio (CR) enables intelligent spectrum sharing through spectrum sensing.
  • Current spectrum sensing methods face challenges in performance and efficiency.

Purpose of the Study:

  • To enhance spectrum sensing performance for dynamic spectrum access.
  • To investigate the application of Machine Learning (ML) algorithms for detecting Fourth Generation (4G) Long-Term Evolution (LTE) signals.
  • To enable efficient utilization of resource blocks by Fifth Generation (5G) new radio systems.

Main Methods:

  • Implementation of k-Nearest Neighbours and Random Forest ML algorithms.
Keywords:
cognitive radioenergy detectionk-nearest neighborsmachine learningrandom forestspectrum sensing

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  • Application of algorithms to Energy Detection (ED) and Energy Vector (EV) data.
  • Analysis of time, frequency, and spatial dependencies in communication traffic data.

    • Main Results:

    • ML algorithms significantly improve spectrum sensing performance with carefully selected training data.
    • Both ED and EV data inputs were examined for their effectiveness.
    • Advantages and disadvantages of real-life application of ML methods were analyzed.

    Conclusions:

    • Machine learning offers a promising approach to enhance spectrum sensing.
    • The choice of training data is critical for the success of ML-based spectrum sensing.
    • The proposed methods can facilitate efficient spectrum sharing between 4G LTE and 5G NR systems.