Volume 29 Issue 6
Dec.  2020
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LI He, ZHAO Wenjing, LIU Chang, JIN Minglu, YOO Sang-Jo. A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues[J]. Chinese Journal of Electronics, 2020, 29(6): 1201-1206. doi: 10.1049/cje.2020.10.007
Citation: LI He, ZHAO Wenjing, LIU Chang, JIN Minglu, YOO Sang-Jo. A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues[J]. Chinese Journal of Electronics, 2020, 29(6): 1201-1206. doi: 10.1049/cje.2020.10.007

A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues

doi: 10.1049/cje.2020.10.007
Funds:  This work is supported by the Ministry of Science, ICT (MSIT), South Korea, under the Information Technology Research Center (ITRC) support program (IITP-2019-2014-1-00729) supervised by the Institute of Information and communications Technology Planning and Evaluation (IITP).
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  • Corresponding author: JIN Minglu (corresponding author) was born in Tumen, Jilin. He received the B.S degree from the University of Science and Technology of China, Hefei, China, in 1982, the M.S. and Ph.D. degrees from Beijing University of Aeronautics and Astronautics, Beijing, China, in 1984 and 1995, respectively. He was a visiting scholar in the Arimoto Laboratory, Osaka University, Osaka, Japan, from 1987 to 1988. He was a Research Fellow in Radio and Broadcasting Research Laboratory, Electronics Telecommunications Research Institute, Daejeon, South Korea, from 2001 to 2004. He is currently a Professor at Dalian University of Technology, Dalian, China. His research interests include wireless communication, wireless sensor networks, and signal processing for wireless communication system. (Email:mljin@dlut.edu.cn)
  • Received Date: 2019-10-15
  • Publish Date: 2020-12-25
  • The existing Goodness of fit (GoF) test based spectrum sensing algorithms mostly use samples or energies as observations to make decisions, which can hardly achieve satisfactory performance especially when the Primary user (PU) signals are highly correlated. Meanwhile, the eigenvalue of covariance matrix can reflect signal correlations well. Motivated by this, we study the distribution of eigenvalue and propose an eigenvalue based GoF spectrum sensing algorithm. In the proposed scheme, we use the ratios of maximum to minimum eigenvalue as observations and thus it can bring performance improvements through capturing correlation of PU signals. We also provide the related theoretical analysis for the proposed method. Simulation results show that the proposed method overcomes the problem of noise uncertainty and achieves performance improvement over the classical samples-based GoF test.
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