WET  Vol.4 No.3 , July 2013
Spectrum Sensing and AM-FM Decomposition through Synchrosqueezing
Abstract: In this paper we have accomplished one of the tasks of cognitive radio i.e. dynamic spectrum sensing by using wavelet based Synchrosqueezing transform [1], a novel technique, which was proposed to analyze a signal in time-frequency plane. The distinctive feature of this transform compared to other techniques is that it enables us to decompose amplitude and frequency modulated signals and allows individual reconstruction of these components. The objective is also to separate the occupied band into amplitude modulated and frequency modulated bands.
Cite this paper: K. Vandhana, P. Sowmya, P. Roshni, K. Divya, S. Ashwin and K. Narayanankutty, "Spectrum Sensing and AM-FM Decomposition through Synchrosqueezing," Wireless Engineering and Technology, Vol. 4 No. 3, 2013, pp. 134-138. doi: 10.4236/wet.2013.43020.

[1]   I. Daubechies, J. F. Lu1 and H.-T. Wu, “Synchrosqueezed Wavelet Transforms: An Empirical Mode Decomposition-Like Tool,” 25 July 2010.

[2]   S. Haykin and L. Fellow, “Cognitive Radio: Brain Empowered Wireless Communications,” IEEE Journal, Vol. 23, No. 2, 2005.

[3]   Gianfelici, F. Biagetti, G. Crippa and P. Turchetti, “AM-FM Decomposition of Speech Signals: An Asymptotically Exact Approach Based on the Iterated Hilbert Transform,” IEEE Conference Publication, July 2005, pp. 333-338.

[4]   T. Backstrom, “Parametric AM/FM Decomposition for Speech and Audio Coding,” IEEE Conference Publication, October 2009, pp. 333-336.

[5]   T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communication Surveys & Tutorials, Vol. 11, No. 1, 2009. doi:10.1109/SURV.2009.090109

[6]   S. Meignen and V. Perrier, “A New Formulation for Empirical Mode Decomposition Based on Constraint Optimization,” 2007.

[7]   G. Thakur, E. Brevdo, N. S. Fuˇckar and H.-T. Wu, “The Synchrosqueezing Algorithm for Time-Varying Spectral Analysis: Robustness Properties and New Paleoclimate Applications,” 2012. arXiv:1105.0010v2[math.CA]

[8]   P. Flandrin, “Time-Frequency/Time-Scale Analysis,” Academic Press, San Diego, 1999.

[9]   A. Graps, “An Introduction to Wavelets,” IEEE Computational Science and Engineering, Vol. 2, No. 2, 1995. doi:10.1109/99.388960

[10]   M. Clausel, T. Oberlin1 and V. Perrier, “The Monogenic Synchrosqueezed Wavelet Transform: A Tool for the Decomposition/Demodulation of AM-FM Images,” 2012.