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Cognitive Radio Sensing Using Hilbert Huang Transform

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DOI: 10.4236/wet.2010.11006    4,196 Downloads   8,304 Views   Citations


Vast segments of the frequency spectrum are reserved for primary (licensed) users. These legacy users often un-der-utilize their reserved spectrum thus causing bandwidth waste. The unlicensed (secondary) users can take advantage of this fact and exploit the spectral holes (vacant spectrum segments). Since spectrum occupancy is transient in nature it is imperative that the spectral holes are identified as fast as possible. To accomplish this, we propose a novel adaptive spectrum sensing procedure. This procedure scans a wideband spectrum using Hilbert Huang Transform and detects the spectral holes present in the spectrum.

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The authors declare no conflicts of interest.

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K. Narayanankutty, A. Nair, D. Soori, D. Pradeep, V. Teja and V. K.B., "Cognitive Radio Sensing Using Hilbert Huang Transform," Wireless Engineering and Technology, Vol. 1 No. 1, 2010, pp. 36-40. doi: 10.4236/wet.2010.11006.


[1] J. Mitola and G. Q. Maguire, “Cognitive Radio: Making Software Radios More Personal,” IEEE Personal Com-munications Magazine, Vol. 6, No. 4, 1999, pp. 13-18.
[2] Federal Communications Commission, Et docket no. 03-322, Dec.
[3] I. F. Akyildiz, W.-Y. Lee, M. C. Vuran, S. Mohanty, “Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey,” Computer Net-works, Vol. 50, No. 13, 2006, pp 2127-2159.
[4] Z. Quan, S. Cui, A. H. Sayed and H. V. Poor, “Wideband Spectrum Sensing in Cognitive Radio Networks,” Pro-ceedings of IEEE Conference Communication, Beijing, May 2008, pp. 901-906.
[5] Z. Quan, S. Cui, A. H. Sayed and H. V. Poor, “Spatial-Spectral Joint Detection for Wideband Spectrum Sensing in Cognitive Radio Networks,” Proceedings IEEE International Conference Acoustic, Speech, Signal Processing (ICASSP), Las Vegas, April 2008, pp. 2793- 2796.
[6] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, C. C. Tung and H. H. Liu, “The Empirical Mode Decomposition Method and The Hilbert Spectrum for Non-Stationary Time Series Analysis,” Proceedings of the Royal Society London, Vol. 454, No. 1971, 1998, pp. 903-995.
[7] L. Cohen, “Time-Frequency Analysis,” Prentice Hall, New York, 1995.
[8] P. Flandrin, “Time-Frequency/Time-Scale Analysis,” Aca-demic Press, San Diego, 1999.
[9] K. Gr?chenig, “Foundations of Time-Frequency Analy-sis,” Birkhauser, Boston, 2001.
[10] H. Tong, “Nonlinear Time Series Analysis,” Oxford University Press, Oxford, 1990.
[11] H. Kantz and T. Schreiber, “Nonlinear Time Series Anal-ysis,” Cambridge University Press, Cambridge, 1990.
[12] C. Diks, “Nonlinear Time Series Analysis: Methods and Applications,” World Scientific Press, Singapore, 1999.
[13] N. E. Huang, S. R. Long and Z. Shen, “The Mechanism for Frequency Downshifts in Nonlinear Wave Evolution,” Advances in Applied Mechanics, Vol. 32, 1996, pp. 59-111.
[14] N. E. Huang, Z. Shen and S. R. Long, “A New View of Water Waves—The Hilbert Spectrum,” Annual Review of Fluid Mechanics, Vol. 31, No. 3, 1999, pp. 417-457.
[15] N. E. Huang, M. C. Wu, S. R. Long, S. S. P. Shen, W. Qu, P. Gloersen and K. L. Fan, “A Confidence Limit for Em-pirical Mode Decomposition and Hilbert Spectral Analy-sis,” Proceedings of the Royal Society London A, Vol. 459, No. 2037, 2003, pp. 2317-2345.
[16] Z. H. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method,” Advanced in Adaptive Data Analysis, Vol. 1, No. 1, 2009, pp. 1-41.
[17] J. Gimenez and L. Marquez, “SVMTool Technical Manual v1.3,” 2006.

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