Dual-Feature Spectrum Sensing Exploiting Eigenvalue and Eigenvector of the Sampled Covariance Matrix

  • Yanping Chen
  • Yulong GaoEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


The signal can be charactered by both eigenvalues and eigenvectors of covariance matrix. However, the existing detection methods only exploit the eigenvalue or eigenvector. In this paper, we utilize both eigenvalues and eigenvectors of the sampled covariance matrix to perform spectrum sensing for improving the detection performance. The features of eigenvalues and eigenvectors are considered integratedly, and the relationship between the false-alarm probability and the decision threshold is offered. To testify this method, some simulations are carried out. The results demonstrate that the method shows some advantages in the detection performance over the conventional method only adapting eigenvalues or eigenvectors.


Dual-feature Spectrum sensing Cognitive radio Eigenvalue and eigenvector 



This work is supported by National Natural Science Foundation of China (NSFC) (Grant No. 61671176).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Harbin University of CommerceHarbinChina
  2. 2.Harbin Institute of TechnologyHarbinChina

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