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.
Similar content being viewed by others
References
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.
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.
Cabric, D. (2008). Addressing feasibility of cognitive radios. IEEE Signal Processing Magazine,25(6), 85–93. https://doi.org/10.1109/MSP.2008.929367.
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.
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.
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.
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.
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.
Narieda, S. (2017). Design of energy detection based on selection diversity combining in cognitive radio.
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 2013—Proceedings, pp. 127–132. https://doi.org/10.1109/iccve.2013.6799781.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Taherpour, Abbas, & Masoumeh Nasiri-Kenari, S. G. (2010). Multiple antenna spectrum sensing in cognitive radios. IEEE Transactions on Wireless Communications,9(2), 814–823.
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.
Crawley, M. J. (2012). Survival analysis. The R Book,100, 869–892. https://doi.org/10.1002/9781118448908.ch27.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-019-06819-8