Browse > Article

A Robust Spectrum Sensing Method Based on Localization in Cognitive Radios  

Kang, Hyung-Seo (울산대학교 전기전자정보시스템공학부)
Koo, In-Soo (울산대학교 전기전자정보시스템공학부)
Publication Information
Journal of Internet Computing and Services / v.12, no.1, 2011 , pp. 1-10 More about this Journal
Abstract
The spectrum sensing is one of the fundamental functions to realize the cognitive radios. One of problems in the spectrum sensing is that the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome the problem, cooperative spectrum sensing method is proposed, which uses a distributed detection model and can increase sensing performance. However, the performance of cooperative spectrum sensing can be still affected by the interference factors such as obstacle and malicious user. Especially, most of cooperative spectrum sensing methods only considered the stationary primary user. In the ubiquitous environment, however the mobile primary users should be considered. In order to overcome the aforementioned problem, in this paper we propose a robust spectrum detection method based on localization where we estimate the location of the mobile primary user, and then based on the location and transmission range of primary user we detect interference users if there are, and then the local sensing reporting from detected interference users are excluded in the decision fusion process. Through simulation, it is shown that the sensing performance of the proposed scheme is more accurate than that of conventional other schemes
Keywords
localization; cognitive radio; spectrum sensing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N.Patwari, J. N. ash, S.Kyperountas, Hero, A.O., III Moses, R.L. Correal, N.S , "Locating the nodes", IEEE Signal Processing Mag., vol.22, no 4, pp. 54-69, july 2005.   DOI
2 An Xun, Jiang Ting and Zhou Zheng, "Centroid localization algorithm for wireless sensor networks," Computer Engineering and Applications, 43(20), pp.136-138, 2007.
3 J. A. Costal, N. Patwari, and A. O. Hero, "Distributed weighted multidimensional scaling for node localization in sensor networks," ACM Trans. Sensor Netw., vol. 2, no. 1, pp. 39-64, Feb. 2006.   DOI
4 L. Lam and C. Y. Suen. Increasing expens for majority vote in ocr: Theoretical considerations and strategies. In Proceedings of the 4th International Workshop on Fronriers in Handwriting Recognition, pp. 245-254, 1994.
5 J. Mitola, "Cognitive radio: an integrated agent architecture for software defined radio", Ph.D thesis, Royal Institute for Technology (KTH) and Sweden
6 S. Haykin, "Cognitive radio: brain-empowered wireless communication, "IEEE Select. Areas Commun., vol. 23, no. 2, Feb. 2005, pp. 201-220.
7 FCC, "Notice of proposed rule making and order : Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies," ET Docket No. 03-108, Feb. 2005.
8 Z. Chair and P. K. Varshney, "Optimum data fusion in multiple sensor detection systems," IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-22, Issue 1, pp. 98-101, Jan. 1986.   DOI
9 D. Cabric, S.M. Mishra, R. Brodersen "Implementation Issues in Spectrum Sensing for Cognitive Radios" Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, Vol. 1, pp 772-776, Nov. 2004.