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Spectrum Sensing Method Based on Information Geometry and Deep Neural Network

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School of Automation, Guangdong University of Technology, Guangzhou 510006, China
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Hubei Key Laboratory of Intelligent Wireless Communications, South-Central University for Nationalities, Wuhan 430074, China
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State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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Author to whom correspondence should be addressed.
Entropy 2020, 22(1), 94; https://doi.org/10.3390/e22010094 (registering DOI)
Received: 11 December 2019 / Revised: 2 January 2020 / Accepted: 10 January 2020 / Published: 12 January 2020
Due to the scarcity of radio spectrum resources and the growing demand, the use of spectrum sensing technology to improve the utilization of spectrum resources has become a hot research topic. In order to improve the utilization of spectrum resources, this paper proposes a spectrum sensing method that combines information geometry and deep learning. Firstly, the covariance matrix of the sensing signal is projected onto the statistical manifold. Each sensing signal can be regarded as a point on the manifold. Then, the geodesic distance between the signals is perceived as its statistical characteristics. Finally, deep neural network is used to classify the dataset composed of the geodesic distance. Simulation experiments show that the proposed spectrum sensing method based on deep neural network and information geometry has better performance in terms of sensing precision. View Full-Text
Keywords: spectrum sensing; information geometry; statistical manifold; geodesic distance; deep neural network spectrum sensing; information geometry; statistical manifold; geodesic distance; deep neural network
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Du, K.; Wan, P.; Wang, Y.; Ai, X.; Chen, H. Spectrum Sensing Method Based on Information Geometry and Deep Neural Network. Entropy 2020, 22, 94.

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