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2018 | OriginalPaper | Buchkapitel

A Spectrum Sensing Algorithm Based on Information Geometry and K-medoids Clustering

verfasst von : Yonghua Wang, Qiang Chen, Jiangfan Li, Pin Wan, Shuiling Pang

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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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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Nächstes Kapitel A Spectrum Sensing Method Based on Null Space Pursuit Algorithm and FCM Clustering Algorithm

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Titel: A Spectrum Sensing Algorithm Based on Information Geometry and K-medoids Clustering
verfasst von: Yonghua Wang
Qiang Chen
Jiangfan Li
Pin Wan
Shuiling Pang
Verlag: Springer International Publishing
Buch: Cloud Computing and Security
Print ISBN: 978-3-030-00005-9

Electronic ISBN: 978-3-030-00006-6

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00006-6_19

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