An Optimal Eigenvalue Based Spectrum Sensing Algorithm for Cognitive Radio

Main Article Content

Gevira Omondi Vitalis K. Oduol

Abstract

Spectrum is a scarce resource, and licensed spectrum is intended to be used only by the spectrum owners. Various measurements of spectrum utilization have shown unused resources in frequency, time and space. Cognitive radio is a new concept of reusing licensed spectrum in an unlicensed manner. The unused resources are often referred to as spectrum holes or white spaces. These spectrum holes could be reused by cognitive radios, sometimes called secondary users. All man-made signals have some structure that can be potentially exploited to improve their detection performance. This structure is intentionally introduced for example by the channel coding, the modulation and by the use of space-time codes. This structure, or correlation, is inherent in the sample covariance matrix of the received signal. In particular the eigenvalues of the sample covariance matrix have some spread, or in some cases some known features that can be exploited for detection. This work aims to implement, evaluate, and eventually improve on algorithms for efficient computation of eigenvalue-based spectrum sensing methods. The computations will be based on power methods for computation of the dominant eigenvalue of the covariance matrix of signals received at the secondary users. The proposed method endeavors to overcome the noise uncertainty problem, and perform better than the ideal energy detection method. The method should be used for various signal detection applications without requiring the knowledge of the signal, channel and noise power.

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How to Cite
Omondi, G., & Oduol, V. (2015). An Optimal Eigenvalue Based Spectrum Sensing Algorithm for Cognitive Radio. International Journal for Innovation Education and Research, 3(10). Retrieved from http://ijier.net/ijier/article/view/444
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Articles
Author Biographies

Gevira Omondi, University of Nairobi, Kenya

School of Engineering

Vitalis K. Oduol, University of Nairobi, Kenya

School of Engineering

References

[1] Joseph Mitola and G. Q. Maguire. Cognitive Radios: Making software radios more personal. IEEE Personal Communications., vol. 6, no. 4, pp. 13-18, 1999.
[2] E. Larsson and M. Skoglund. Cognitive radio in a frequency-planned environment: some basic limits. IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 4800-4806, December 2008.
[3] N. Hoven and A. Sahai. Power scaling for cognitive radio. International Conference on Wireless Networks, Communications and Mobile Computing, pp. 250255 vol.1, 13-16 June 2005.
[4] A. Sahai and N. Hoven and R. Tandra. Some fundamental limits on cognitive radio. Allerton Conference on Communication, Control, and Computing, October 2004, pp. 16621671.
[5] D. Cabric and A. Tkachenko and R. W. Brodersen. Spectrum sensing measurements of pilot, energy, and collaborative detection. Proc.Military Communications Conference (MILCOM), pp. 1-7, Oct. 2006.
[6] H.-S. Chen and W. Gao and D. G. Daut. Signature based spectrum sensing algorithms for IEEE 802.22 WRAN. Proc. IEEE Intern. Conf.Communications (ICC), June 2007.
[7] R. Tandra and A. Sahai. Fundamental limits on detection in low SNR under noise uncertainty. IEEE International Conference on Wireless Networks, Communications and Mobile Computing, June 13-16 2005, vol. 1, pp. 464469.
[8] Bai Z. D. Methodologies in spectral analysis of large dimensional random matrices, a review. Statistica Sinica, Vol. 9, No. 3, 611-677, 1999.
[9] Chowdhury K. R. and M. Di Felice and I. F. Akyildiz. TCP CRAHN: A transport control protocol for cognitive radio ad hoc networks. IEEE Transactions on Mobile Computing, Vol. 12, No. 4, 790-803, April 2013.
[10] Johnstone I.M. On the distribution of the largest eigenvalue in principle components analysis. The Annals of Statistics, Vol. 29, No. 2, 295-327, 2001.
[11] Kortun A. and T. Ratnarajah and M. Sellathurai and C. J. Zhong and C. B. Papadias. On the performance of eigenvalue-based cooperative spectrum sensing for cognitive radio. IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 1, 49-55, February 2011.
[12] Lee W. Y. and I. F. Akyildiz. Spectrum-aware mobility management in cognitive radio cellular networks. IEEE Transactions on Mobile Computing, Vol. 11, No. 4, 529-542, April 2012.
[13] Pillay N. and H. J. Xu. Blind eigenvalue-based spectrum sensing for cognitive radio networks. IET Communications, Vol. 6, No. 11, 13881396, July 2012.
[14] Tufts D. W. and C. D. Mellissinos. Sample, effective computation of principal eigenvectors and their eigenvalues and application to highresolution estimation of frequencies. IEEE Transactions on Acoustics, Speech and Signal Processing, Vol. 34, No. 5, 1046-1053, October 1986.
[15] Tulino A. M. and S. Verd. Random Matrix Theory and Wireless Communications. Now Publishers Inc., Hanover, MA, USA, 2004.
[16] Wang. S. and N.Rahnavard. Eigenvalue-based cooperative spectrum sensing with finite samples/sensors. 46th Annual Conference on Information Sciences and Systems, 1-5, 2012.
[17] Y. Zeng and Y.C. Liang. Eigenvalue-based spectrum sensing algorithms for cognitive radio. IEEE Transactions on Communications, vol. 57, no. 6, pp. 17841793, June 2009.
[18] Zeng Y. H. and Y. C. Liang. Maximum-minimum eigenvalue detection for cognitive radio. Proceedings of the 18th international Symposium on Personal, Indoor and Mobile Radio Communications, 1165-1169, September 2007.
[19] Zeng Y. H. and Y. C. Liang. Robust spectrum sensing in cognitive radio. IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops, 129-134, 2010.
[20] Zeng Y. H. and L. K. Choo and Y. C. Liang. Maximum eigenvalue detection: Theory and Application. IEEE International Conference on Communications, 5076-5080, 2008.