Abstract
In order to improve the performance of existing spectrum sensing methods in cognitive radios and solve the complex problem of decision threshold calculations. This paper uses the information geometry theory and combines the unsupervised learning method of K-medoids clustering to realize the spectrum sensing. Firstly, using the information geometry theory, the statistical characteristics of wireless spectrum signals received by secondary users are analyzed and transformed into geometric characteristics on statistical manifolds. Correspondingly, the sampled signal of the secondary user corresponds to the point on the statistical manifold, and the distance feature between different points is obtained by using a metric method on the manifold. Finally, the K-medoids clustering algorithm is used to classify the distance features and determine whether the primary user signal exists, and achieve the purpose of spectrum sensing. Simulation results show that the proposed method outperforms traditional spectrum sensing algorithms.
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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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Wang, Y., Chen, Q., Li, J., Wan, P., Pang, S. (2018). A Spectrum Sensing Algorithm Based on Information Geometry and K-medoids Clustering. 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_19
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