A Cholesky decomposition and fusion clustering based spectrum sensing method

Jian Li; Yue Li; Xinpei Zhao; Xiaoxu Chen

doi:10.4108/eetsis.9552

Authors

Jian Li Heilongjiang University
Yue Li Heilongjiang University
Xinpei Zhao Heilongjiang University
Xiaoxu Chen Heilongjiang University

DOI:

https://doi.org/10.4108/eetsis.9552

Keywords:

cooperative spectrum sensing, Cholesky, K-means, Gaussian mixture model

Abstract

Spectrum sensing is a key technology to detect unused frequency bands, and is widely applied in spectrum sharing and dynamic channel allocation. However, it is a challenge to provide high sensing accuracy under low signal-to-noise-ratio (SNR) environments. To address this issue, this paper proposes a novel method based on feature extraction and fusion clustering. First, the sampling matrix of the received signal is decomposed into two orthogonal components I and Q, and Cholesky decomposition is performed on the covariance matrices of I and Q components to extract their two-dimensional feature vectors. Then, the fusion clustering algorithm is proposed, where the GMM clustering algorithm is performed to classify the feature vectors, and the initial parameters of GMM, such as centroids, weights and covariance matrices, are generated by K-means clustering. Simulation results show that the proposed method accelerates the convergence speed of GMM and improves the classification accuracy. It effectively enhances the performance of spectrum sensing compared to other mainstream methods.

References

[1] Kaur J. and Kaur K. (2015) A fuzzy approach for an IoT-based automated employee performance appraisal, Computers Materials and Continua, Vol. 53, No. 1, pp. 24–38.

[2] Alemseged Y.D. and Harada H. (2009) Spectrum sensing for cognitive radio, Proceedings of the IEEE, Vol. 97, No. 5, pp. 849–877.

[3] Yu L., Wu Q. and Wang J. (2014) Optimal adaptive multiband spectrum sensing in cognitive radio networks, KSII Transactions on Internet and Information Systems, Vol. 8, pp. 984–996.

[4] Awe O.P., Deligiannis A. and Lambotharan S. (2018) Spatio-temporal spectrum sensing in cognitive radio networks using beamformer-aided SVM algorithms, IEEE Access, Vol. 6, pp. 25377–25388.

[5] Luo J., Zhang G., Xu H., Zhou Y. and Yang W. (2015) An energy detection based signal detection method with multiple antennas in cognitive radio, Proc. IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 187–191.

[6] Surampudi A. and Kalimuthu K. (2016) An adaptive decision threshold scheme for the matched filter method of spectrum sensing in cognitive radio using artificial neural networks, Proc. 1st India International Conference on Information Processing (IICIP), pp. 1–5.

[7] Du J., Huang H., Jing X.J. and Chen X. (2016) Cyclostationary feature based spectrum sensing via low-rank and sparse decomposition in cognitive radio networks, Proc. 16th International Symposium on Communications and Information Technologies (ISCIT), pp. 615–619.

[8] Liu C.; Yang M. (2011) A Distance-Weighed Algorithm Based on Maximum-Minimum Eigenvalues for Cooperative Spectrum Sensing, Proc. IEEE 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Wuhan, China, pp. 1–4.

[9] Zeng Y.; Liang Y. (2009) Eigenvalue-based spectrum sensing algorithms for cognitive radio, IEEE Transactions on Communications, Vol. 57, No. 6, pp. 1784–1793.

[10] Abed V.; Shahzadi A. (2014) A Maximum-Minimum Eigenvalue Detection Simpler Method Based on Secondary Users Locations for Cooperative Spectrum Sensing, Proc. 2014 International Congress on Technology, Communication and Knowledge (ICTCK), Mashhad, Iran, pp. 1–4.

[11] Miah M.S.; Yu H.J.; Godder T.K.; Rahman M.M. (2015) A Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Network Using Eigenvalue Detection Technique with Superposition Approach, International Journal of Distributed Sensor Networks, Vol. 2015, pp. 1–11.

[12] Yang X.; Lei K.; Peng S.; Cao X. (2011) Blind detection for primary user based on the sample covariance matrix in cognitive radio, IEEE Communications Letters, Vol. 15, No. 1, pp. 40–42.

[13] Zhou F.; Li Z.; Si J.; Guan L. (2014) An efficient spectrum sensing algorithm for cognitive radio based on finite random matrix, Proc. IEEE 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1223–1227.

[14] Li Z.; Zhou F.; Si J.; Qi P.; Guan L. (2016) Feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix, IET Communications, Vol. 10, No. 9, pp. 1003–1011.

[15] Bkassiny M.; Li Y.; Jayaweera S.K. (2013) A survey on machine-learning techniques in cognitive radios, IEEE Communications Surveys & Tutorials, Vol. 15, No. 3, pp. 1136–1159.

[16] Lu Y.; Zhu P.; Wang D.; Fattouche M. (2016) Machine learning techniques with probability vector for cooperative spectrum sensing in cognitive radio networks, Proc. IEEEWireless Communications and Networking Conference (WCNC), pp. 1–6.

[17] Sobabe G.C.; Song Y.; Bai X. et al. (2018) A cooperative spectrum sensing algorithm based on unsupervised learning, Proc. 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1–6.

[18] Zhang Y.; Wan P.; Zhang S.; Wang Y.; Li N. (2017) A spectrum sensing method based on signal feature and clustering algorithm in cognitive wireless multimedia sensor networks, Advances in Multimedia, Vol. 2017, Art. ID 2895680, 10 pages.

[19] Wang Y.; Zhang Y.; Zhang S. et al. (2018) A cooperative spectrum sensing method based on a feature and clustering algorithm, Proc. 2018 Chinese Automation Congress (CAC), pp. 1029–1033.

[20] Bao J.; Nie J.; Liu C.; Jiang B.; Zhu F.; He J. (2019) Improved blind spectrum sensing by covariance matrix Cholesky decomposition and RBF-SVM decision classification at low SNRs, IEEE Access, Vol. 7, pp. 97117–97129.