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Machine learning algorithms significantly outperform classical methods for detecting U.S. military radars, like the SPN-43 air traffic control radar, in the 3.5 GHz Citizens Broadband Radio Service band.

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Area of Science:

  • Wireless communication
  • Signal processing
  • Machine learning

Background:

  • Federal Communications Commission rules allow commercial wireless networks to share the 3.5 GHz Citizens Broadband Radio Service (CBRS) band with federal incumbents.
  • Commercial systems must vacate the band upon detection of U.S. military radars, such as the SPN-43 air traffic control radar.
  • Accurate detection algorithms are critical for the reliable operation of these sensing systems.

Purpose of the Study:

  • To evaluate thirteen different methods for detecting the SPN-43 radar within the 3.5 GHz band.
  • To compare the performance of classical signal detection techniques against machine learning and deep learning approaches.
  • To identify the most effective algorithms for radar detection in the CBRS band.

Main Methods:

  • Utilized a dataset of over 14,000 spectrograms from the 3.5 GHz band, collected during a recent measurement campaign.
  • Compared thirteen detection algorithms, including classical signal detection theory methods, machine learning algorithms, and deep learning architectures.
  • Focused on evaluating accuracy and computational complexity trade-offs.

Main Results:

  • Machine learning algorithms demonstrated significantly superior performance compared to classical signal detection methods.
  • A three-layer convolutional neural network (CNN) emerged as the top performer, offering an optimal balance between accuracy and computational cost.
  • The developed CNN was applied to analyze the entire 3.5 GHz spectrogram library, yielding descriptive statistics.

Conclusions:

  • Modern machine learning algorithms, particularly deep learning models like CNNs, show substantial promise for radar detection in the 3.5 GHz CBRS band.
  • Classical detection methods may have limitations in this dynamic spectrum sharing environment.
  • The findings support the adoption of advanced algorithms for robust radar sensing in shared spectrum bands.