A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues

LI He; ZHAO Wenjing; LIU Chang; JIN Minglu; YOO Sang-Jo

doi:10.1049/cje.2020.10.007

Volume 29 Issue 6

Dec. 2020

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2020 > 29(6): 1201-1206

LI He, ZHAO Wenjing, LIU Chang, et al., “A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1201-1206, 2020, doi: 10.1049/cje.2020.10.007

Citation:

LI He, ZHAO Wenjing, LIU Chang, et al., “A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1201-1206, 2020, doi: 10.1049/cje.2020.10.007

Citation:

PDF( 1011 KB)

A Novel Goodness of Fit Test Spectrum Sensing Using Extreme Eigenvalues

doi: 10.1049/cje.2020.10.007

1.
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China;
2.
Department of Information and Communication Engineering, Inha University, Incheon 22212, South Korea

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).

More Information

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

Abstract

Abstract

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.
- Cognitive radio,
- spectrum sensing,
- goodness of fit test,
- eigenvalue

FullText(HTML)

References(18)

References

F. Hu, B. Chen and K. Zhu, "Full spectrum sharing in cognitive radio networks toward 5G:a survey", IEEE Access, Vol.6, pp.15754-15776, 2018.

M. Amjad, M. H. Rehmani and S. Mao, "Wireless multimedia cognitive radio networks:A comprehensive survey", IEEE Communications Surveys and Tutorials, Vol.20, No.2, pp.1056-103, 2018.

A. Ali and W. Hamouda, "Advances on spectrum sensing for cognitive radio networks:Theory and applications", IEEE Communications Surveys and Tutorials, Vol.19, No.2, pp.1277-1304, 2017.

B. Ma, H. Wang and X. Xie, "An improved wideband compressed spectrum sensing scheme based on binomial distribution", Acta Electronica Sinica, Vol.48, No.2, pp.243-248, 2020.

Q. Pei, H. Li and X. Liu, "Neighbor detection-based spectrum sensing algorithm in distributed cognitive radio networks", Chinese Journal of Electronics, Vol.26, No.2, pp.399-406, 2017.

F. Awin, E. Abdel-Raheem and K. Tepe, "Blind spectrum sensing approaches for interweaved cognitive radio system:A tutorial and short course", IEEE Communications Surveys and Tutorials, Vol.21, No.1, pp.238-259, 2018.

H. Urkowitz, "Energy detection of unknown deterministic signals", Proceedings of the IEEE, Vol.55, No.4, pp.523-531, 1967.

Y. Zeng, C. L. Koh and Y. C. Liang, "Maximum eigenvalue detection:Theory and application", Proc. of IEEE International Conference on Communications, Beijing, China, pp.4160-4164, 2008.

Y. Zeng and Y. C. Liang, "Eigenvalue-based spectrum sensing algorithms for cognitive radio", IEEE Transactions on Communications, Vol.57, No.6, pp.1784-1793, 2009.

R. Zhang, T. J. Lim, Y. C. Liang, et al., "Multi-antenna based spectrum sensing for cognitive radios:A GLRT approach", IEEE Transactions on Communications, Vol.58, No.1, pp.84-88, 2010.

C. Liu, H. Li, J. Wang, et al., "Optimal eigenvalue weighting detection for multi-antenna cognitive radio networks", IEEE Transactions on Wireless Communications, Vol.16, No.4, pp.2083-2096, 2017.

S. Rostami, K. Arshad and K. Moessner, "Order-statistic based spectrum sensing for cognitive radio", IEEE Communications Letters, Vol.16, No.5, pp.592-595, 2012.

H. Wang, E. H. Yang, Z. Zhao, et al., "Spectrum sensing in cognitive radio using goodness of fit testing", IEEE Transactions on Wireless Communications, Vol.8, No.11, pp.5427-5430, 2009.

M. Jin, Q. Guo, J. Xi, et al., "Spectrum sensing based on goodness of fit test with unilateral alternative hypothesis", Electronics Letters, Vol.50, No.22, pp.1645-1646, 2014.

Y. He, W. Zhao, C. Liu, et al., "A novel spectrum sensing method using goodness of fit test based on maximum eigenvalue", Journal of Signal Processing, Vol.33, No.3A, pp.33-40, 2017.

Y. Ye, G. Lu, Y. Li, et al., "Unilateral right-tail Anderson Darling test based spectrum sensing for cognitive radio", Electronics Letters, Vol.53, No.18, pp.1256-1258, 2017.

G. Zhang, X. Wang, Y. C. Liang, et al., "Fast and robust spectrum sensing via kolmogorov-smirnov test", IEEE Transactions on Communications, Vol.58, No.12, pp.3410-3416, 2010.

D. Teguig, V. Le Nir and B. Scheers, "Spectrum sensing method based on goodness of fit test using chi-square distribution", Electronics Letters, Vol.50, No.9, pp.713-715, 2014.

Relative Articles

Supplements(0)

Cited By

Proportional views