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Polarization Domain Spectrum Sensing Algorithm Based on AlexNet.

Shiyu Ren1, Hailong Wu1, Wantong Chen1

  • 1School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China.

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|November 26, 2022
PubMed
Summary
This summary is machine-generated.

A new spectrum sensing algorithm, JCM-AlexNet, leverages polarization characteristics for improved signal and noise detection. This method outperforms existing deep learning and conventional algorithms in various conditions.

Keywords:
AlexNetdeep learningpolarization informationspectrum sensing

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Area of Science:

  • Signal Processing
  • Wireless Communications
  • Machine Learning

Background:

  • Spectrum sensing is crucial for efficient wireless communication.
  • Traditional methods face challenges with complex signal environments.
  • Polarization domain offers unique signal and noise characteristics.

Purpose of the Study:

  • To propose a novel spectrum sensing algorithm, JCM-AlexNet.
  • To enhance spectrum sensing performance by utilizing polarization information.
  • To classify signals and noise effectively using deep learning.

Main Methods:

  • Utilizing the Jones vector covariance matrix (JCM) to represent polarization states.
  • Employing the AlexNet deep learning model for feature extraction from JCM images.
  • Calculating a likelihood ratio test statistic (AlexNet-LRT) for classification.

Main Results:

  • The JCM-AlexNet algorithm demonstrated superior performance compared to conventional polarization detection (PSD).
  • JCM-AlexNet outperformed other deep learning algorithms like LeNet5, LSTM, and MLP.
  • Effective performance was observed across varying signal-to-noise ratios and false alarm probabilities.

Conclusions:

  • The JCM-AlexNet algorithm offers a significant advancement in spectrum sensing.
  • Leveraging polarization features with deep learning enhances detection accuracy.
  • This approach provides a robust solution for dynamic spectrum access.