Abstract
In order to improve the sensing performance of spectrum sensing systems in complex environments. This paper proposes a spectrum sensing method based on Null Space Pursuit algorithm (NSP) and fuzzy c-means (FCM) clustering algorithm. The signal sensing by the spectrum system is first pre-processed using a Null Space Pursuit algorithm and the signal is decomposed into sub-signal components with more distinct features. In order to further improve the accuracy of feature estimation the IQ decomposition method is used to process the signal. Then extract the eigenvalues of the signals to form a two dimensional feature vector. Finally, these eigenvectors and the FCM clustering algorithm yield a classifier that uses the classifier to determine the state of the unknown spectrum. In the experimental part, we verify the method in different environments. Experimental results show that the method can effectively improve the sensing performance of spectrum sensing system compared to traditional spectrum sensing methods.
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Acknowledgments
This work was supported in part by special funds from the central finance to support the development of local universities under No. 400170044, the project supported by the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences under grant No. 20180106, the science and technology program of Guangdong Province under grant No. 2016B090918031, the degree and graduate education reform project of Guangdong Province under grant No. 2016JGXM_MS_26, the foundation of key laboratory of machine intelligence and advanced computing of the Ministry of Education under grant No. MSC-201706A and the higher education quality projects of Guangdong Province and Guangdong University of Technology.
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Zhang, Y., Wang, Y., Wan, P., Zhang, S., Li, N. (2018). A Spectrum Sensing Method Based on Null Space Pursuit Algorithm and FCM Clustering Algorithm. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11063. Springer, Cham. https://doi.org/10.1007/978-3-030-00006-6_20
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DOI: https://doi.org/10.1007/978-3-030-00006-6_20
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