Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
A Template Matching Approach to Classification of QAM Modulation using Genetic Algorithm
Reza Berangi, Negar ahmadi
Pages - 95 - 109     |    Revised - 30-10-2009     |    Published - 30-11-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
Automatic Modulation Recognition, Constellation diagram, Genetic Algorithm, Template Matching
The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real-world. In this paper modulation classification for QAM is performed by Genetic Algorithm followed by Template matching, considering the constellation of the received signal. In addition this classification finds the decision boundary of the signal which is critical information for bit detection. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.
CITED BY (4)  
1 Alvarez, J. L. B., & Montero, F. E. H.Clasificación automática de modulaciones mpsk utilizando cumulantes de octavo orden.
2 Alharbi, H., Mobien, S., Alshebeili, S., & Alturki, F. (2012). Automatic modulation classification of digital modulations in presence of HF noise.Eurasip Journal on Advances in Signal Processing, 2012(1), 1-14.
3 Hazza, A., Shoaib, M., Saleh, A., & Fahd, A. (2011). Robustness of digitally modulated signal features against variation in HF noise model.Eurasip Journal on Wireless Communications and Networking, 2011(1), 1-12.
4 Ahmadi, N., & Berangi, R. (2010). Symbol Based Modulation Classification using Combination of Fuzzy Clustering and Hierarchical Clustering. Signal Processing–An International Journal (SPIJ), 4(2), 123.
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Bielefeld Academic Search Engine (BASE) 
10 Scribd 
11 WorldCat 
12 SlideShare 
14 PdfSR 
1 J. Lopatka, M. Pedzisz. “Automatic Modulation Classification using Statistical Moments and a Fuzzy Classifier”, Signal Processing Proceedings, WCCC- ICSP 2000, 5th international conf. on, 3:1500-1506, 21-25 Aug. 2000
2 Y. O. Al-Jalili. “Identification Algorithm of Upper Sideband and Lower Sideband SSB Signals”, Signal Processing, 42:207-213, 1995
3 L. Narduzzi, M. Bertocco. “Conformance and Performance”, Department of Electronic and Informatics, Pavova University, 2003
4 J. Reichert. “Automatic Classification of Communication Signals using Higher Order Statistics”, ICASSP 92, 221-224,1992
5 R. Schalkoff, “Pattern Recognition: Statistical, Structural and Neural Approach” , John Wiley, (1992)
6 Bijan G. Mobaseri. “Constellation shape as a robust signature for digital modulation recognition”, Military Communications Conference Proceedings, MILCOM IEEE, 1:442-446, 1999
7 Bijan G. Mobasseri. “Digital Modulation Classification using Constellation Shape”, Signal Processing, 80(2):251-277,2000
8 F. Jondral. “Automatic Classification of High Frequency Signals”, Signal Processing, 9(3):177-190,1985
9 L. Dominguez, J. Borrallo, J. Garcia. “A General Approach to the Automatic Classification of Radiocommunication Signals”, Signal Processing, 22(3):239-250,1991
10 F.F. Liedtke. “Computer Simulation of an Automatic Classification Procedure for Digitally Modulated Communication Signals with Unknown Parameters”, Signal Processing, 6:311-323,1984
11 J. Aisbett. “Automatic Modulation Recognition using Time-Domain Parameters”, Signal Processing, 13(3):323-329,1987
12 A. Polydoros, K. Kim. “On the Detection and Classification of Quadrature Digital Modulation in Broad-Band Noise”, IEEE Transactions on Communications, 38(8):1199-1211,1990
13 C. Huang, A. Polydoros. “Likehood Method for MPSK Modulation Classification”, IEEE Transaction on Communications, 43(2/3/4):1493-1503,1995
14 S. Soliman, S. Hsue. “Signal classification using statistical moments”, IEEE Transactions on Communications, 40(5):908-915,1992
15 W. Wei, J. Mendel. “A New Maximum Likelihood for Modulation Classification” Asilomar-29, 1132-1138, 1999
16 K. Chugg, et al. “Combined Likelihood Power Estimation and Multiple Hypothesis Modulation Classification”, Asilomar-29, 1137-1141, 1996
17 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.
18 Nhi P. Ta. “A Wavelet Packet Approach to Radio Signal Classification”, symposium on Time-Frequency and Time Scale Analysis, 508-511, 1994
19 Linhu Zhao, Yasuhiro Tsujihiura, Mitsuo Gien. “Genetic algorithm for fuzzy clustering”, Proceedings of IEEE International Conference on, 716–719, 1996
Mr. Reza Berangi
- Iran
Mr. Negar ahmadi
- Iran