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Reseach Article

Cognitive Radio: A Gram-Charlier based Non-parametric Approach in the Context of Spectrum Hole Search

Published on December 2013 by Srijibendu Bagchi, Mahua Rakshit
2nd International conference on Computing Communication and Sensor Network 2013
Foundation of Computer Science USA
CCSN2013 - Number 3
December 2013
Authors: Srijibendu Bagchi, Mahua Rakshit
3c8f4128-70fa-4da4-9087-56e3cb7fdd8c

Srijibendu Bagchi, Mahua Rakshit . Cognitive Radio: A Gram-Charlier based Non-parametric Approach in the Context of Spectrum Hole Search. 2nd International conference on Computing Communication and Sensor Network 2013. CCSN2013, 3 (December 2013), 26-30.

@article{
author = { Srijibendu Bagchi, Mahua Rakshit },
title = { Cognitive Radio: A Gram-Charlier based Non-parametric Approach in the Context of Spectrum Hole Search },
journal = { 2nd International conference on Computing Communication and Sensor Network 2013 },
issue_date = { December 2013 },
volume = { CCSN2013 },
number = { 3 },
month = { December },
year = { 2013 },
issn = 0975-8887,
pages = { 26-30 },
numpages = 5,
url = { /proceedings/ccsn2013/number3/15462-1338/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 2nd International conference on Computing Communication and Sensor Network 2013
%A Srijibendu Bagchi
%A Mahua Rakshit
%T Cognitive Radio: A Gram-Charlier based Non-parametric Approach in the Context of Spectrum Hole Search
%J 2nd International conference on Computing Communication and Sensor Network 2013
%@ 0975-8887
%V CCSN2013
%N 3
%P 26-30
%D 2013
%I International Journal of Computer Applications
Abstract

Cognitive radio arises to be tempting solution to the spectral congestion problem by introducing opportunistic usage of the frequency bands that are not heavily occupied by licensed users. Spectrum sensing plays a crucial role in the cognitive radio technology to prevent damaging interference to the primary users and to reliably and quickly spot the white spaces in the spectrum and utilize the opportunity. In energy detection based spectrum sensing technique, the noise distribution as well as the signal plus noise distribution is assumed to be Gaussian. In reality, however, it is often difficult to validate these underlying assumptions with the available data. In this paper, an approach has been made by considering signal plus noise distribution as non-Gaussian and approximated with the generalized Gram-Charlier Type A series. The probability of detection is given for Gram-Charlier series for a fixed false alarm probability.

References
  1. Haykin S, Feb 2005 “Cognitive Radio: Brain-empowered wireless communications”, IEEE J. Selected Areas in Communications, vol.23, no. 2, pp. 201-220.
  2. FCC, December 2003 “In the Matter of Facilitating Opportunities for Flexible, Efficient and Reliable Spectrum Use Employing Cognitive Radio Technologies” ET Docket No.03-108.
  3. Akyildiz I.F, LeeW.Y, VuranM.C. and Mohanty S. May 2006 “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey” Computer Networks, Elsevier, vol. 50, pp. 2127-2159.
  4. ArslanH. and YücekT. 2007 “Spectrum Sensing for Cognitive Radio Applications, “Cognitive Radio, Software Defined Radio, and Adaptive Wireless Systems, H. Arslan, ed. Springer, pp. 263-289.
  5. Chen K.C. and Prasad R, 2009 “Cognitive Radio Networks”, John Wiley & Sons. Ltd.
  6. Geirhofer S., Tong L. and SadlerB.M, May 2007 “Dynamic Spectrum Access in the Time Domain: Modeling and Exploiting White Space”, IEEE Communications Magazine, pp. 66-72.
  7. GhesamiA. and SousaE.S, April 2008.“Spectrum Sensing in Cognitive Radio Networks: Requirements, Challenges and Design Trade-offs”, IEEE Communications Magazine, pp. 32-39.
  8. ShinK.G, KimH, MinA.W. and KumarA. December 2010 “Cognitive Radios for Dynamic Spectrum Access: From Concept to Reality”, IEEE Wireless Communications, pp. 64-74.
  9. TabakovicZ. GrgicS.and GrgicM.2009“Dynamic Spectrum Access in Cognitive Radio”, 51st International Symposium EL,pp. 245-248, Zadar, Croatia.
  10. Urkowitz H. 1967 “Energy detection of unknown deterministic signals,” Proc.IEEE, vol. 55, no. 4, pp. 523–531.
  11. Cabric D., TkachenkoA and BrodersenR.W. 2006 “Spectrum Sensing Measurements of Pilot, Energy and Collaborative Detection”, IEEE Military Communications Conference (MILCOM)
  12. Cabric D., TkachenkoA and BrodersenR.W. 2006”Experimental Study of Spectrum Sensing based on energy Detection and Network Cooperation, Proc. Of 1st Intl. Workshop on Technology and Policy for Accessing Spectrum (TAPAS), Boston.
  13. Sahai A and Cabric D, November 2005 “A tutorial on spectrum sensing: Fundamental limits and practical challenges”, Proc. IEEE Symp. New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD.
  14. Tandra R and Sahai A 2008 “SNR Walls for Signal Detection”, IEEE J. Selected Topics in Signal Processing, vol. 2, no. 1, pp. 4-17.
  15. Tandra R and Sahai A 2005”Fundamental Limits on Detection in Low SNR Under Noise Uncertainty”, International Conference on Wireless Networks, Communication and Mobile Computing.
  16. Wang H, Yang E, Zhao Z and Zhang W 2009 “Spectrum Sensing in Cognitive Radio Using Goodness of Fit Testing” IEEE Transactions on Wireless Communications, vol. 8, no. 11, pp. 5427-5430.
  17. Kendall M.G. and Stuart A., “The Advanced Theory of Statistics”, vol. 1, Charles Griffin & Company Limited, London.
  18. GibbonsJ.D. 1971 “Nonparametric Statistical Inference”, McGraw-Hill.
Index Terms

Computer Science
Information Sciences

Keywords

Cognitive radio false alarm probability Gram Charlier series probability of detection spectrum hole