Abstract
Making using of the emerging information geometry theory, we analyze the statistical properties of wireless spectrum signals received by secondary users, and propose a cognitive radio spectrum sensing method based on information geometry. We introduce a new detection structure, using the sample covariance matrix and corresponding to the points on the statistical manifold, by calculating the distance between them and make a decision, thus transforming the statistical detection problem into the geometric problem on the manifold. We also used two solutions: Constant False Alarm Rate (CFAR) Detector and Distance Detector (DD). The simulation results reveal that the performance of the information geometry method is superior to the traditional spectrum sensing algorithm, and research results will help us to explore the spectrum sensing problem from a new perspective.
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Acknowledgements
This work was supported in part by the Science and Technology Program of Guangdong Province under Grant No. 2016B090918031, Degree and Graduate Education Reform Project of Guangdong Province under Grant No. 2016JGXM_MS_26, Foundation of Key Laboratory of Machine Intelligence and Advanced Computing of the Ministry of Education under Grant No. MSC-201706A and Higher Education Quality Project of Guangdong Province.
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Chen, Q., Wan, P., Wang, Y., Li, J., Xiao, Y. (2017). Research on Cognitive Radio Spectrum Sensing Method Based on Information Geometry. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_47
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DOI: https://doi.org/10.1007/978-3-319-68542-7_47
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