Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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Author to whom correspondence should be addressed.
Academic Editor: Huseyin Arslan
Sensors 2017, 17(4), 661; https://doi.org/10.3390/s17040661
Received: 4 January 2017
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Revised: 16 March 2017
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Accepted: 20 March 2017
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Published: 23 March 2017
(This article belongs to the Section Sensor Networks)
Detecting the signals of the primary users in the wideband spectrum is a key issue for cognitive radio networks. In this paper, we consider the multi-antenna based signal detection in a wideband spectrum scenario where the noise statistical characteristics are assumed to be unknown. We reason that the covariance matrices of the spectrum subbands have structural constraints and that they describe a manifold in the signal space. Thus, we propose a novel signal detection algorithm based on Riemannian distance and Riemannian mean which is different from the traditional eigenvalue-based detector (EBD) derived with the generalized likelihood ratio criterion. Using the moment matching method, we obtain the closed expression of the decision threshold. From the considered simulation settings, it is shown that the proposed Riemannian distance detector (RDD) has a better performance than the traditional EBD in wideband spectrum sensing.
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Keywords:
cognitive radio; wideband spectrum sensing; information geometry; Riemannian distance; Riemannian mean; moment matching
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MDPI and ACS Style
Lu, Q.; Yang, S.; Liu, F. Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks. Sensors 2017, 17, 661. https://doi.org/10.3390/s17040661
AMA Style
Lu Q, Yang S, Liu F. Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks. Sensors. 2017; 17(4):661. https://doi.org/10.3390/s17040661
Chicago/Turabian StyleLu, Qiuyuan, Shengzhi Yang, and Fan Liu. 2017. "Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks" Sensors 17, no. 4: 661. https://doi.org/10.3390/s17040661
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