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Proposition of Minimum Eigenvalue Algorithm in Cognitive Radio Technology

Amir Eslami, Gunes Karabulut Kurt
Istanbul Technical University Istanbul, Turkey

Abstract: In the cognitive radio technology, the non-reliance property to signal and noise power is deployed. In this work we propose a one to minimum eigenvalue spectrum algorithm with the help of the Tracy-WIdom distribution. This proposed method has not followed the noise power. Limiting distribution approach to minimum eigenvalue causes the detection algorithm to perform much better in lower SNR ranges. It has been shown that this system outperforms better not only in low SNR range but also in low smoothing factors when compared to well-known maximum to minimum eigenvalue method. Finally, we have assessed the performance improvement and compared with other well-known algorithms in a wide SNR range and different smoothing factors which proves the efficiency.

Keywords: Spectrum Sensing, Cognitive Radio Technology, Maximum to Minimum Eigenvalue Method, Energy Detection, Energy and One to Minimum Eigenvalue Method Proposition of Minimum Eigenvalue Algorithm in Cognitive Radio Technology

DOI:https://doi.org/10.6025/jitr/2021/12/1/1-7

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References:

[1] Eslami, A., Karamzadeh, S. (2016). Proposal and Analysis of a New Spectrum Sensing Algorithm for Cognitive Radio Driven Hospitals, International Journal of Applied Mathematics, Electronics and Computers,. 4, p 48-51, 2016.
[2] Zeng, Y. H., Liang, Y. C. (2009). Eigenvalue-based Spectrum Sensing Algorithms for Cognitive Radio, IEEE Transactions on Communications, vol. 57, p 1784-1793, 2009.
[3] Ye, Z., Memik, G., Grosspietsch, J. (2008). Energy Detection using Estimated Noise Variance for Spectrum Sensing in Cognitive Radio Networks, Wireless Communications and Networking Conference 2008.
[4] Pillay, N., Xu, H. J. (2012). Blind Eigenvalue-based Spectrum Sensing for Cognitive Radio Networks, IET Communications, 6 (11), p 1388-1396, July 2012.
[5] Lopez-Benitez, M., Casadevall, F. (2012). Improved Energy Detection Spectrum Sensing for Cognitive Radio, IET Commun., 6 (8), p 785-796, May 2012.
[6] Feldheim, O. N., Sodin. S. (2010). A Universality Result for the Smallest Eigenvalues of Certain Sample Covariance Matrices, Geometric and Functional Analysis, 20 (1), p 88-123, 2010.
[7] Xu, W., Sanzheng, Q. (2008). A Fast Symmetric SVD Algorithm for Square Hankel Matrices, Linear Algebra and its Applications, 428 (2-3), p 550-563, 2008.
[8] Penna, F., Garello, R., Spirito, M. (2009). Cooperative Spectrum Sensing Based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices, IEEE Commun. Lett., 13 (7), p 507- 509, July 2009.
[9] Eslami, A., Karamzadeh, S. (2016). Double Threshold Maximum to Minimum Eigen-value Spectrum Sensing Proposal and Performance Analysis, 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), p 646- 649, 2016.
[10] Wael, C.B.A., Armi, N., Rohman, B.P. (2016). Spectrum Sensing for low SNR Environment using Maximum-Minimum Eigenvalue (MME) Detection, 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), p 435- 438, 2016.

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