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A Comparison of Spectrum-Sensing Algorithms Based on Eigenvalues
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 Title & Authors
A Comparison of Spectrum-Sensing Algorithms Based on Eigenvalues
Ali, Syed Sajjad; Liu, Jialong; Liu, Chang; Jin, Minglu;
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Cognitive radio has been attracting increased attention as an effective approach to improving spectrum efficiency. One component of cognitive radio, spectrum sensing, has an important relationship with the performance of cognitive radio. In this paper, after a summary and analysis of the existing spectrum-sensing algorithms, we report that the existing eigenvalue-based semi-blind detection algorithm and blind detection algorithm have not made full use of the eigenvalues of the received signals. Applying multi-antenna systems to cognitive users, we design a variety of spectrum-sensing algorithms based on the joint distribution of the eigenvalues of the received signal. Simulation results validate that the proposed algorithms in this paper are able to detect whether the signal of the primary user exists or not with high probability of detection in an environment with a low signal-to-noise ratio. Compared with traditional algorithms, the new algorithms have the advantages of high detection performance and strong robustness
Cognitive Radio;Eigenvalues;Joint Distribution;Multiple antennas;Spectrum Sensing;
 Cited by
Federal Communications Commission, “Spectrum Policy Task Force Report,” Washington, DC, ET Docket No. 02-135, 2002.

J. Mitola, "Cognitive radio: an integrated agent architecture for software defined radio," Ph.D. dissertation, Royal Institute of Technology (KTH), Stockholm, Sweden, 2000.

T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, 2009. crossref(new window)

IEEE 802.22.1-2010 Standard for the Enhanced Interference Protection of the Licensed Devices [Internet], Available:

S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory. Englewood Cliffs, NJ: PTR Prentice-Hall, 1998.

A. Sahai and D. Cabric, "Spectrum sensing: fundamental limits and practical challenges," in Proceedings of IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Maui, HI, 2005.

H. Urkowitz, Energy detection of unknown deterministic signals,” Proceedings of the IEEE, vol. 55, no. 4, pp. 523-531, 1967. crossref(new window)

Y. Zeng and Y. C. Liang, "Maximum-minimum eigenvalue detection for cognitive radio," in Proceedings of IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC2007), Athens, Greece, pp. 1-5, 2007.

P. Wang, J. Fang, N. Han, and H. Li, “Multiantenna-assisted spectrum sensing for cognitive radio,” IEEE Transactions on Vehicular Technology, vol. 59, no. 4, pp. 1791-1800, 2010. crossref(new window)

A. M. Tulino and S. Verdu, Random Matrix Theory and Wireless Communications. Hanover, MA: Now Publishers Inc., 2004.

M. Rudelson and R. Vershynin, "Non-asymptotic theory of random matrices: extreme singular values," in Proceedings of the International Congress of Mathematicians (ICM'10), Hyderabad, India, 2010.