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Symbol Based Modulation Classification using Combination of Fuzzy Clustering and Hierarchical Clustering
Negar ahmadi, Reza Berangi
Pages - 123 - 137     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Fuzzy C-means, AMR, Modulation Classification, Hierarchical Clustering
Most of approaches for recognition and classification of modulation have been founded on modulated signal’s components. In this paper, we develop an algorithm using fuzzy clustering and consequently hierarchical clustering algorithms considering the constellation of the received signal to identify the modulation types of the communication signals automatically. The simulation that has been conducted shows high capability of this method for recognition of modulation levels in the presence of noise and also, this method is applicable to digital modulations of arbitrary size and dimensionality. In addition this classification finds the decision boundary of the signal which is critical information for bit detection.
CITED BY (4)  
1 Twayana, K. S., & Joshi, S. R. (2016). Fuzzy Clustering Based Blind Adaptive OFDM System. Journal of Advanced College of Engineering and Management, 1, 51-58.
2 Cheng, L., Xi, L., Zhao, D., Tang, X., Zhang, W., & Zhang, X. (2015). Improved modulation format identification based on Stokes parameters using combination of fuzzy c-means and hierarchical clustering in coherent optical communication system. Chinese Optics Letters, 13(10), 100604.
3 El-Khamy, S. E., & Elsayed, H. A. (2012). Classification of Multi-User Chirp Modulation Signals Using Wavelet Higher-Order-Statistics Features and Artificial Intelligence Techniques. Int'l J. of Communications, Network and System Sciences, 5(09), 520.
4 Elsayed, H., El-Khamy, S. E., & Rizk, M. M. (2011, August). Higher order statistics classification of multi-user chirp modulation signals using clustering techniques. In General Assembly and Scientific Symposium, 2011 XXXth URSI (pp. 1-4). IEEE.
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A. Polydoros, K. Kim, “On the Detection and Classification of Quadrature Digital Modulation in Broad-Band Noise”, IEEE Transactions on Communications, Vol. 38, No. 8, pp. 1199-121, 1990
A. Swami, B. M. Sadler, “Hierarchical Digital Modulation Classification Using Cumulants”, IEEE Trans. Communications, Vol. 48(3), pp.416-429, 2000
A.K. Nandi, E.E. Azzouz, “Algorithms for Automatic Modulation Recognition of Communication Signals”, IEEE Trans. Communications ,Vol. 46(4), pp. 431-436, April 1998
Bijan G. Mobaseri, “Constellation shape as a robust signature for digital modulation recognition”, Military Communications Conference Proceedings, MILCOM IEEE, Volume 1, Issue, pp. 442-446, 1999
Bijan G. Mobasseri, “Digital Modulation Classification using Constellation Shape”, Signal Processing, Vol. 80, No. 2, pp.251-277, 2000
C. Huang, A. Polydoros, “Likehood Method for MPSK Modulation Classification”, IEEE Transaction on Communications, Vol. 43, No. 2/3/4, pp.1493-1503, 1995
Daniel Boudreau, Christian Dubuc, Francois Patenaude et al., “A Fast Automatic Modulation Recognition Algorithm and Its Implementation in a Spectrum Monitoring Application”, MILCOM2000, Los Angeles, California, Oct. 22-25, 2000
E. Gose, R. Johnsonbaugh, S. Jost, “Pattern Recognition and Image Analysis”, Prentice Hall PTR, (1996)
E.E. Azzouz, A.K.Nandi., “Automatic Modulation Recognition of Communication Signals”, Kluwer Academic Publisher, Norwell, MA, 1996
F. Jondral, “Automatic Classification of High Frequency Signals”, Signal Processing, Vol. 9, No. 3, pp.177-190, 1985
F.F. Liedtke, “Computer Simulation of an Automatic Classification Procedure for Digitally Modulated Communication Signals with Unknown Parameters”, Signal Processing, Vol. 6, pp.311.323, 1984
Frank Chung-Hoon Rhee and Cheul Hwang, “A Type-2 Fuzzy C-Means Clustering Algorithm”, 20 th NAFIPS international conference, Vol. 4, pp. 1926-1929, 2001
J. Aisbett, “Automatic Modulation Recognition using Time-Domain Parameters”, Signal Processing, Vol.13, No. 3, pp.323-329, 1987
J. Lopatka, M.Pedzisz, “Automatic Modulation Classification Using Statistical Moments and a Fuzzy Classifier”, in Proceedings of ICSP2000, pp 1500-1506, 2000
J. Reichert, “Automatic Classification of Communication Signals using Higher Order Statistics”, ICASSP 92, pp.221-224, 1992
K. Chugg, et al, “Combined Likelihood Power Estimation and Multiple Hypothesis Modulation Classification”, Asilomar-29, pp. 1137-114, 1996
Krishna K. Chintalapudi and Moshe Kam, “A Noise-Resistant Fuzzy C Means Algorithm for Clustering”, Fuzzy systems proceedings, IEEE international Conference, Vol. 2, pp. 1458-1463, 1998
L. Dominguez, J. Borrallo, J. Garcia, “A General Approach to the Automatic Classification of Radiocommunication Signals”, Signal Processing, Vol. 22, No. 3, pp.239-250, 1991
Negar Ahmadi, Reza Berangi, “A Template Matching Approach to Classification of QAM Modulation using Genetic Algorithm”, Signal Processing: An International Journal, Vol.3, Issue 5, pp: 95-109, 2009
R. Schalkoff, “Pattern Recognition: Statistical, Structural and Neural Approach” , John Wiley, (1992)
S. G. Wilson, “Digital Modulation and Coding”, New York, Prentice-Hall, Inc., Ch.3, (1996)
S. Soliman, S. Hsue, “Signal classification using statistical moments”, IEEE Transactions on Communications, Vol. 40, No. 5, pp. 908-915, 1992
Sadaaki Miyamoto, “An Overview and New Methods in Fuzzy Clustering”, Knowledge-Based Intelligent Electronic Systems, Second International Conference, Vol. 1, , pp. 33-40, 1998.
W. Wei, J. Mendel, “A New Maximum Likelihood for Modulation Classification,” Asilomar-29, pp. 1132-1138, 1996
Wen Wei, Jerry M.Mendel, “A Fuzzy Logic Method for Modulation Classification in Nonideal Environments”, IEEE Trans. Fuzzy Systems, Vol.7 (3), pp.333-344, June 1999
Y.Lin, C.C. Kuo, “Classification of Quadrature Amplitude Modulated (QAM) Signals via Sequential Probability Ratio Test (SPRT)”, Report of CRASP, University of Southern California, July 15, 1996
Mr. Negar ahmadi
- Iran
Dr. Reza Berangi
- Iran