Skip to main content
Log in

Multiple Antenna Spectrum Sensing Based on GLR Detector in Cognitive Radios

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As is known, eigenvalue based spectrum sensing for multi-antenna cognitive radio systems is a highly preferred method because it requires no priori knowledge concerning the signal to be sensed. The performance of the method depends on the most accurate calculation of the test statistic and the threshold value. Regaining of the threshold values was aimed in this study in order to increase performance in generalized likelihood ratio detection based sensing. While the threshold value was calculated, the probability of false detection (Pfa) and the probability of detection were regained by using a new probability distribution function for Wishart matrices. The simulations were performed under noise uncertainty for MIMO–OFDM systems, and a noticeable performance increase was observed with the new threshold value in comparison with conventional methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Mitola, J. (1999). Cognitive radio for flexible mobile multimedia communications. In Mobile multimedia communications, 1999. (MoMuC’99) 1999 IEEE international workshop on, 22102, 3–10. https://doi.org/10.1109/momuc.1999.819467.

  2. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications,6(4), 13–18. https://doi.org/10.1109/98.788210.

    Article  Google Scholar 

  3. Cabric, D. (2008). Addressing feasibility of cognitive radios. IEEE Signal Processing Magazine,25(6), 85–93. https://doi.org/10.1109/MSP.2008.929367.

    Article  Google Scholar 

  4. Salahdine, F., Ghazi, H. El, Kaabouch, N., & Fihri, W. F. (2015). Matched filter detection with dynamic threshold for cognitive radio networks. In 2015 international conference on wireless networks and mobile communications (WINCOM), pp. 1–6. https://doi.org/10.1109/wincom.2015.7381345.

  5. Zhang, X., Chai, R., & Gao, F. (2014). Matched filter based spectrum sensing and power level detection for cognitive radio network. In 2014 IEEE global conference on signal and information processing (GlobalSIP), pp. 1267–1270. https://doi.org/10.1109/globalsip.2014.7032326.

  6. Gibson, A., & Zafar, J. (2008). Cyclostationary spectrum detection in cognitive radios. IET Seminar on Cognitive Radio and Software Defined Radio: Technologies and Techniques,1(1), 17. https://doi.org/10.1049/ic:20080398.

    Article  Google Scholar 

  7. Aparna, P. S., & Jayasheela, M. (2012). Cyclostationary feature detection in cognitive radio using different modulation schemes. International Journal of Computer Applications,47(21), 975–8887. https://doi.org/10.7763/IJFCC.2013.V2.249.

    Article  Google Scholar 

  8. Zeng, Yonghong, Liang, Ying-Chang, & Zhang, Rui. (2008). Blindly combined energy detection for spectrum sensing in cognitive radio. IEEE Signal Processing Letters,15, 649–652. https://doi.org/10.1109/LSP.2008.2002711.

    Article  Google Scholar 

  9. Narieda, S. (2017). Design of energy detection based on selection diversity combining in cognitive radio.

  10. Annamalai, A., & Olaluwe, A. (2013). On the energy detection of unknown signals in κ-μ And η-μ fading channels with diversity receivers. In 2013 international conference on connected vehicles and expo, ICCVE 2013Proceedings, pp. 127–132. https://doi.org/10.1109/iccve.2013.6799781.

  11. Maaref, A. (2007). Joint and marginal eigenvalue distributions of (Non)central complex wishart matrices and PDF-based approach for characterizing the capacity statistics of MIMO ricean and rayleigh fading channels. IEEE Transactions on Wireless Communications,6(10), 3607–3619. https://doi.org/10.1109/TWC.2007.06028.

    Article  Google Scholar 

  12. Chiani, M. (2014). Distribution of the largest eigenvalue for real Wishart and Gaussian random matrices and a simple approximation for the Tracy–Widom distribution. Journal of Multivariate Analysis,129, 69–81. https://doi.org/10.1016/j.jmva.2014.04.002.

    Article  MathSciNet  MATH  Google Scholar 

  13. Zeng, Y., & Liang, Y. C. (2009). Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications,57(6), 1784–1793. https://doi.org/10.1109/TCOMM.2009.06.070402.

    Article  Google Scholar 

  14. Pillay, N., & Xu, H. J. (2012). Blind eigenvalue-based spectrum sensing for cognitive radio networks. IET Communications,6(11), 1388. https://doi.org/10.1049/iet-com.2011.0506.

    Article  MathSciNet  MATH  Google Scholar 

  15. Deo, R. S. (2016). On the Tracy–Widom approximation of studentized extreme eigenvalues of Wishart matrices. Journal of Multivariate Analysis,147, 265–272. https://doi.org/10.1016/j.jmva.2016.01.010.

    Article  MathSciNet  MATH  Google Scholar 

  16. He, Y., Ratnarajah, T., Yousif, E. H. G., Xue, J., & Sellathurai, M. (2016). Performance analysis of multi-antenna GLRT-based spectrum sensing for cognitive radio. Signal Processing,120, 580–593. https://doi.org/10.1016/j.sigpro.2015.10.018.

    Article  Google Scholar 

  17. Zheng, K., Zhao, L., Mei, J., Shao, B., Xiang, W., & Hanzo, L. (2015). Survey of large-scale MIMO systems. IEEE Communications Surveys and Tutorials,17(3), 1738–1760. https://doi.org/10.1109/COMST.2015.2425294.

    Article  Google Scholar 

  18. Jin, M., Guo, Q., Xi, J., Member, S., & Li, Y. (2015). Spectrum sensing using weighted covariance matrix in Rayleigh fading channels. IEEE Transactions on Vehicular Technology,64(11), 5137–5148. https://doi.org/10.1109/TVT.2014.2379924.

    Article  Google Scholar 

  19. Qin, S., Zhang, W., Xiong, H., & Chen, D. (2015). Cooperative spectrum sensing using finite Demmel condition numbers. Wireless Personal Communications,80(1), 335–346. https://doi.org/10.1007/s11277-014-2012-8.

    Article  Google Scholar 

  20. Charan, C., & Paney, R. (2016). Eigenvalue based double threshold spectrum sensing under noise uncertainty for cognitive radio. Optik,127(15), 5968–5975. https://doi.org/10.1016/j.ijleo.2016.04.049.

    Article  Google Scholar 

  21. Shakil, M., & Ahsanullah, M. (2016). Characterizations of the distribution of the Demmel condition number of real Wishart matrices. Special Matrices,4(1), 352–365. https://doi.org/10.1515/spma-2016-0034.

    Article  MathSciNet  MATH  Google Scholar 

  22. Nadler, B. (2011). On the distribution of the ratio of the largest eigenvalue to the trace of a Wishart matrix. Journal of Multivariate Analysis,102(2), 363–371. https://doi.org/10.1016/j.jmva.2010.10.005.

    Article  MathSciNet  MATH  Google Scholar 

  23. Johnstone, I. M. (2001). On the distribution of the largest eigenvalue in principal components analysis. Annals of Statistics,29(2), 295–327. https://doi.org/10.1214/aos/1009210544.

    Article  MathSciNet  MATH  Google Scholar 

  24. Taherpour, Abbas, & Masoumeh Nasiri-Kenari, S. G. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications,9(2), 814–823.

    Article  Google Scholar 

  25. Kortun, A., Sellathurai, M., Ratnarajah, T., & Zhong, C. (2012). Distribution of the ratio of the largest eigenvalue to the trace of complex Wishart matrices. IEEE Transactions on Signal Processing,60(10), 5527–5532. https://doi.org/10.1109/TSP.2012.2205922.

    Article  MathSciNet  MATH  Google Scholar 

  26. Crawley, M. J. (2012). Survival analysis. The R Book,100, 869–892. https://doi.org/10.1002/9781118448908.ch27.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fatih Yavuz Ilgin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Çiflikli, C., Ilgin, F.Y. Multiple Antenna Spectrum Sensing Based on GLR Detector in Cognitive Radios. Wireless Pers Commun 110, 1915–1927 (2020). https://doi.org/10.1007/s11277-019-06819-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06819-8

Keywords

Navigation