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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)

Abstract

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.

Keywords

Dual-feature Spectrum sensing Cognitive radio Eigenvalue and eigenvector 

Notes

Acknowledgments

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

References

  1. 1.
    Abdelmohsen A, Hamouda W. Advances on spectrum sensing for cognitive radio networks: theory and applications. IEEE Commun Surv Tutorials. 2017;19(2):1277–304.CrossRefGoogle Scholar
  2. 2.
    Guo H, Jiang W, Luo W. Linear soft combination for cooperative spectrum sensing in cognitive radio networks. IEEE Commun Lett. 2017;21(7):1573–6.CrossRefGoogle Scholar
  3. 3.
    Wang B, Liu KJR. Advances in cognitive radio networks: a survey. IEEE J Sel Top Sig Process. 2011;5(1):5–23.CrossRefGoogle Scholar
  4. 4.
    Mitola J, Maguire GQ. Cognitive radio: making software radios more personal. IEEE Pers Commun. 1999;6(4):13–8.CrossRefGoogle Scholar
  5. 5.
    Mchenry M, Livsics E, Nguyen T, Majumdar N. XG dynamic spectrum access field test results [Topics in radio communications]. IEEE Commun Mag. 2007;45(6):51–7.CrossRefGoogle Scholar
  6. 6.
    Liu Chang, Li Ming, Jin Ming-Lu. Blind energy-based detection for spatial spectrum sensing. IEEE Wirel Commun Lett. 2015;4(1):91–8 Feb.Google Scholar
  7. 7.
    Cabric D, Brodersen RW. Physical layer design issues unique to cognitive radio systems. Proc IEEE Pimrc. 2005;2:759–63.Google Scholar
  8. 8.
    Cabric D, Tkachenko A, Brodersen R. Spectrum sensing measurements of pilot, energy, and collaborative detection. In: IEEE Conference on Military Communications; 2006. p. 1–7Google Scholar
  9. 9.
    Sonnenschein A, Fishman PM. Radiometric detection of spread-spectrum signals in noise of uncertain power. IEEE Trans Aerosp Electron Syst. 1992;28(3):654–60.CrossRefGoogle Scholar
  10. 10.
    Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Commun Surv Tutorials. 2009;11(1):116–30.CrossRefGoogle Scholar
  11. 11.
    An T, Kim D, Song I, et al. Cooperative spectrum sensing based on generalized likelihood ratio test under impulsive noise circumstances. In: 2012 IEEE military communications conference; 2012. p. 1–6Google Scholar
  12. 12.
    Cardoso LS, Debbah M, Bianchi P, et al. Cooperative spectrum sensing using random matrix theory. In: International symposium on wireless pervasive computing; 2008. p. 334–8Google Scholar
  13. 13.
    Zeng Y, Liang YC. Maximum-minimum eigenvalue detection for cognitive radio. In: IEEE international symposium on personal, indoor and mobile radio communications; 2007. p. 1–5Google Scholar
  14. 14.
    Zhang P, Qiu R, Guo N. Demonstration of spectrum sensing with blindly learned features. IEEE Commun Lett. 2011;15(5):548–50.CrossRefGoogle Scholar
  15. 15.
    Hou S, Qiu RC. Kernel feature template matching for spectrum sensing. IEEE Trans Veh Technol. 2014;63(5):2258–71.CrossRefGoogle Scholar
  16. 16.
    Eldar YC, Chan AM. On the asymptotic performance of the decorrelator. IEEE Trans Inf Theor. 2003;49(9):2309–13.MathSciNetCrossRefGoogle Scholar

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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