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September 10, 2025SensorsOpen Access

Spectrum Sensing for Noncircular Signals Using Augmented Covariance-Matrix-Aware Deep Convolutional Neural Network

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Authors

SCSonglin ChenUniversity of Electronic Science and Technology of ChinaZHZhenqing HeSichuan UniversityWSWenze SongSichuan University

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Overview

This approach improves spectrum sensing of noncircular signals in cognitive radio networks, enhancing detection performance.

Key Points

  • The proposed method shows significant improvement in detecting noncircular signals in cognitive radio networks.
  • Experimental results reveal enhanced detection performance compared to existing benchmark methods.
  • Using an augmented covariance matrix allows the deep learning model to fully capture signal characteristics.
  • This approach eliminates restrictive model assumptions, making the method more robust.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c1b19354b1d3bfb60e8bcehttps://doi.org/10.3390/s25154791
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Cognitive radio: making software radios more personal1999 · 9,190 citations
  2. 2Spectrum Sensing for Cognitive Radio2009 · 815 citations
  3. 3Robust Spectrum Sensing for Noncircular Signal in Multiantenna Cognitive Receivers2014 · 44 citations
  4. 4Detection of Rank-$P$ Signals in Cognitive Radio Networks With Uncalibrated Multiple Antennas2011 · 181 citations
  5. 5Blind Cooperative Parametric Spectrum Sensing With Distributed Sensors Using Local Average Power Passing2016 · 11 citations