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SETSCI - Volume 4 (6) (2019)
ISAS WINTER-2019 (ENS) - 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences, Samsun, Turkey, Nov 22, 2019

Effect of Non-Gaussian Noise on Detection Performance of Multi-Stage Detectors
Timur Düzenli1*, Olcay Akay2
1Amasya University, Amasya, Turkey
2Dokuz Eylül University, İzmir, Turkey
* Corresponding author: timur.duzenli@amasya.edu.tr
Published Date: 2019-12-22   |   Page (s): 408-411   |    193     5
https://doi.org/10.36287/setsci.4.6.105

ABSTRACT It is well known that non-Gaussian noise deteriorates the detection performances of the conventional detection methods. This problem is also encountered in cognitive radio where impulsive structure of noise negatively affects spectrum sensing. Multistage detection techniques can be considered as a possible solution as they analyze signal samples using different detection techniques in a sequential way. In this study, spectrum sensing performance of a multistage detection method is investigated for detection of the primary user signal contaminated by non-Gaussian noise. At first, observed signal samples are analyzed by energy detector which is followed by absolute value cumulation. Then, a decision as to whether the channel is in use or not is given based on the result of this analysis. Accordingly, if any one of those techniques detects the primary user signal, then the channel is assigned as busy.
KEYWORDS Signal detection, Multi-stage detection, Energy detector, Absolute value cumulation, Spectrum sensing, Cognitive radio
REFERENCES [1] J. Mitola and G.Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, pp. 13-18, Aug. 1999.
[2] J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio,” PhD Thesis, Royal Institute of Technology, Sweden, 2000.
[3] T. Yücek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, pp. 116–130, 2009.
[4] I. F. Akyildiz, W. Y. Lee, M. C. Vuran and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey,” Computer Networks, vol. 50, pp. 2127–2159, 2006.
[5] S. Haykin, “Cognitive radio: Brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, pp. 201-220, 2005.
[6] V. A. Aalo, A-Bon E. Ackie, C. Mukasa, “Performance analysis of spectrum sensing schemes based on fractional lower order moments for cognitive radios in symmetric α-stable noise environments,” Signal Processing, vol. 154, pp. 363-374, 2019.
[7] K. Hassan, R. Gautier, I. Dayoub, M. Berbineau and E. Radoi, “Multiple-antenna based blind spectrum sensing in the presence of impulsive noise,” IEEE Transactions on Vehicular Technology, vol. 63, pp. 2248-2257, 2014.
[8] H. G. Kang, I. Song, S. Yoon and Y. H. Kim, “A class of spectrum-sensing schemes for cognitive radio under impulsive noise circumstances: Structure and performance in nonfading and fading environments,” IEEE Transactions on Vehicular Technology, vol. 59, pp. 4322-4339, 2010.
[9] K. Chang and B. Senadji, “Spectrum sensing optimisation for dynamic primary user signal,” IEEE Trans. Commun., vol. 60, pp. 3632-3640, 2012.
[10] T. Düzenli and O. Akay, “A new spectrum sensing strategy for dynamic primary users in cognitive radio,” IEEE Comm. Letters, vol. 20, no. 4, pp. 752-755, 2016.
[11] L. Tang, Y. Chen, E. L Hines, and M.-S. Alouini, “Performance analysis of spectrum sensing with multiple status changes in primary user traffic,” IEEE Communications Letters, vol. 16, pp. 874-877, 2012.
[12] Y. Chen, C. Wang, and B. Zhao, “Performance comparison of feature-based detectors for spectrum sensing in the presence of primary user traffic,” IEEE Signal Processing Letters, vol. 18, pp. 291-294, 2011.
[13] T. Düzenli and O. Akay, “A new method of spectrum sensing in cognitive radio for dynamic and randomly modelled primary users,”
IETE Journal of Research, To be published. DOI: 10.1080/03772063.2019.1628668
[14] S. MacDonald, D. C. Popescu, and O. Popescu, “Analyzing the performance of spectrum sensing in cognitive radio systems with dynamic primary user activity,” IEEE Communications Letters, vol. 21, pp. 2037-2040, 2017.
[15] T. Wang, Y. Chen, E. L. Hines, and B. Zhao, “Analysis of effect of primary user traffic on spectrum sensing performance,” in Proc. IEEE International Conference on Communications and Networking in China (ChinaCOM 2009), China, 2009, pp. 1–5.
[16] T. Düzenli and O. Akay, “Effect of non-Gaussian noise and primary user traffic on detection performance in cognitive radio,” in 26th Signal Processing and Communications Applications Conference (SIU), İzmir, Turkey, 2018.
[17] J. Mu, X. Jing, J. Xie, Y. Zhang, “Multistage spectrum sensing scheme with SNR estimation,” IET Communications, vol. 13, pp. 1148-1154, 2019.
[18] M. Hamid, N. Björsell and S. B. Slimane, “Energy and eigenvalue based combined fully blind self-adapted spectrum sensing algorithm,” IEEE Transactions on Vehicular Technology, vol. 65, pp. 630-642, 2016.
[19] R. Gao, Z. Li, H. Li and B. Ai, “Absolute value cumulating based spectrum sensing with Laplacian noise in cognitive radio networks,” Wireless Personal Communications, vol. 83, pp. 1387–1404, 2015.
[20] Y. Ye, Y. Li, G. Lu, F. Zhou and H. Zhang, “Performance of spectrum sensing based on absolute value cumulation in Laplacian noise,” IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, Canada, 2017.
[21] L. Jiang, Y. Li, Y. Ye, Y. Chen, M. Jin and H. Zhang, “Unilateral left-tail Anderson Darling test-based spectrum sensing with Laplacian noise,” IET Communications, vol. 13, 696-705, 2019.
[22] F. Tan, X. Song, C. Leung and J. Cheng, “Collaborative spectrum sensing in a cognitive radio system with Laplacian noise,” IEEE Communications Letters, vol. 16, pp. 1691-1694, 2012.
[23] Y. Ye, Y. Li, G. Lu and F. Zhou, “Improved energy detection with Laplacian noise in cognitive radio,” IEEE Systems Journal, vol. 13, pp. 18-29, 2019.
[24] S. M. Kay, Fundamentals of Statistical Signal Processing Vol. II:
Detection Theory, New Jersey, USA: Prentice Hall, 1998. ISBN-13:
978-0135041352


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