2011

Classification of analog modulated communication signals using clustering techniques: A comparative study

In this paper, a comparative study of classification of the analog modulated communication signals using clustering techniques is introduced. Four different clustering algorithms are implemented for classifying the analog signals.These clustering techniques are K-means clustering, fuzzy c-means clustering, mountain clustering and subtractive clustering. Two key features are used for characterizing the analog modulation types. Performance comparison of these clustering algorithms is made using computer simulations.

PDF

___

1.F. Jondral, Automatic classification of high frequency signals, Signal Processing, 9, 177-190, 1985.
2.J. Aisbett, Automatic modulation recognition using time-domain parameters, Signal Processing, 13,323-329, 1987.
3.L.Dominiguez, J. Borallo, J. Garcia, A general approach to automatic classification of radiocommunication signals, Signal Processing, 22, 239-250, 1991.
4.A. Polydoros, K. Kim, On the detection and classification of quadrature digital modulations in broad¬band noise, IEEE Transactions on Communications, 38, 1199-1211,1990.
5.S.S. Soliman, S. Hsue, Signal classification using statistical moments. IEEE Transactions on Communications, 40, 908-915, 1992.
6.N. Ghani, R. Lamontagne, Neural networks applied to the classification of spectral features for automatic modulation recognition, Military Communication Conference (MILCOM'93), 111-115,1993.
7.E.E. Azzouz, A.K. Nandi, Automatic Modulation Recognition of Communication Signals, Kluwer Academic Publishers, 1996.
8.A.K. Jain, R.C. Dubes, Algorithms for Clustering Data, Prentice-Hall, Englewood Cliffs, NJ, 1988.
9.U. Maulik, S. Bandyopadhyay, Genetic Algorithm based clustering technique. Pattern Recognition 33, 1455-1465,2000.
10.R. Kothari, D. Pitts, On finding the number of clusters, Pattern Recognition Letters, 20, 405-416,1999.
11.T.W.Liao. Clustering of time series data—a survey, Pattern Recognition, In Press.
12.J. S.R. Jang, C.T. Sun, E. Mizutani, Neuro-fuzzy and soft computing, Prentice Hall, 1997.
13.R.O. Duda, P.E. Hart, Pattern Recognition and scene analysis, John Willey and Sons, 1973.
14.J.C. Bezdek, Fuzzy mathematics in pattern classification, PhD thesis, Applied Math. Center, Cornell University Ithaca, 1973.
15.R.R. Yager, D .P. Filev, Generation of fuzzy rules by mountain clustering, IEEE Transactions on System, Man and Cybernetics, 2, 209-219, 1994.
16.S. L. Chiu, Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems, 2, 1994.