Secure Cooperative Spectrum Sensing based on the Distance between Bodies of Evidence

Yi Wang, Yanjun Hu, Xiwen Tang, Yingguan Wang

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DOI: 10.4236/ijcns.2012.51008                

Abstract

In cognitive radio (CR) networks, unlicensed secondary users need to conduct spectrum sensing to gain access to a licensed spectrum band. And cooperation among CR users will solve the problems caused by multipath fading and shadowing. In this paper, we propose a multi-threshold method at local nodes to cope with noises of great uncertainty. Functions of distance between bodies of evidence are used at fusion centre to make synthetic judgment. To guarantee security which is an essential component for basic network functions, we will take selfish nodes into account which try to occupy channels exclusively. The proposed technique has shown better performance than conventional algorithms without increase the system overhead.

Keywords

Cognitive Radio; Spectrum Sensing; Multiple Thresholds; Energy Detection; Evidence Theory

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	T. Yucek and H. Aralan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communications Surveys & Tutorials, Vol. 11, No. 1, 2009, pp. 116-130. doi:10.1109/SURV.2009.090109
[2]	S. Hussain and X. Femando, “Spectrum Sensing in Cognitive Radio Networks: Up-to-Date Techniques and Future Challenges,” IEEE International Conference on Science and Technology for Humanity, Toronto, 26-27 September 2009, pp. 736-741.
[3]	N. Nguyen-Thanh and Insoo Koo, “Log-likehood Ratio Optimal Quantizer for Cooperative Spectrum Sensing in Cognitive Radio,” IEEE Communications Society, Vol. 15, No. 2, 2011, pp. 317-319.
[4]	Z. Quan, S. G. Cui and A. H. Sayed, “Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio networks,” Selected Topics in Signal Processing, Vol. 2, No. 1, 2008, pp. 28-40. doi:10.1109/JSTSP.2007.914882
[5]	C. M. Qi, J. Wang and S. Q. Li, “Weighted-Clustering Cooperative Spectrum Sensing in Cognitive Radio Context,” WRI International Conference on Communications and Mobile Computing, Yunnan, 6-8 January 2009, pp. 102-106. doi:10.1109/CMC.2009.303
[6]	X. Y. Wang, A. Wong and P. H. Ho, “Extended Knowledge-Based Reasoning Approach to Spectrum Sensing for Cognitive Radio,” IEEE Mobile Computing, Vol. 9, No. 4, 2010, pp. 465-478. doi:10.1109/TMC.2009.148
[7]	R. L. Chen, J. M. Park and K. Bian, “Robust Distributed Spectrum Sensing in Cognitive Radio Networks,” The 27th Conference on Computer Communication, Phoenix, 13-18 April 2008, pp. 1876-1884.
[8]	M. Z. Win, P. C. Pinto and L. A. Shepp, “A Mathematical Theory of Network interference and Its Applications,” Proceedings of the IEEE, Vol. 97, No. 2, 2009, pp. 205-230. doi:10.1109/JPROC.2008.2008764
[9]	G. Shafer, “A Mathematical Theory of Evidence,” Princeton University Press, Princeton, 1976.
[10]	N. Nguyen-Thanh and I. Koo, “Evidence-Theory-Based Cooperative Spectrum Sensing with Efficient Quantization Method in Cognitive Radio,” Vehicular Technology, Vol. 60, No. 1, 2011, pp. 185-195. doi:10.1109/TVT.2010.2086501
[11]	Q. H. Peng, K. Zeng, J. Wang and S. Q. Li, “A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context,” IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, Helsinki, 11-14 September 2006, pp. 1-5.
[12]	E. Lefevre, O. Colot, P. Vannoorenberghe and D. de Brucq, “A Generic Framework for Resolving the Conflict in the Combination of Belief Structures,” Proceedings of the Third International Conference on Information Fusion, Paris, Vol. 1, 10-13 July 2000, pp. 11-18.
[13]	H. Urkowitz, “Energy Detection of Unknown Deterministic Signal,” Proceedings of the IEEE, Vol. 55, No. 4, 1967, pp. 523-531. doi:10.1109/PROC.1967.5573
[14]	A. Jousselme, D. Grenier and E. Bosse, “A New Distance between Two Bodies of Evidence,” Information Fusion, Vol. 2, No. 2, 2001, pp. 91-101. doi:10.1016/S1566-2535(01)00026-4