Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier


Automatic Digital Modulation Recognition (ADMR) is becoming an interesting problem with various civil and military applications. In this paper, an ADMR algorithm in Multi-Carrier Code Division Multiple Access (MC-CDMA) systems using Discrete Transforms (DTs) and Mel-Frequency Cepstral Coefficients (MFCCs) is proposed. This algorithm uses various DT techniques such as the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST) with MFCCs to extract features from the modulated signal and a Support Vector Machine (SVM) to classify the modulation orders. The proposed algorithm avoids over fitting and local optimal problems that appear in Artificial Neural Networks (ANNs). Simulation results shows the classifier is capable of recognizing the modulation scheme with high accuracy up to 90% - 100% using DWT, DCT and DST for some modulation schemes over a wide Signal-to-Noise Ratio (SNR) range in the presence of Additive White Gaussian Noise (AWGN) and Rayleigh fading channel, particularly at a low Signal-to-Noise ratios (SNRs).

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M. Keshk, E. Elrabie, F. El-Samie and M. El-Naby, "Blind Modulation Recognition in Wireless MC-CDMA Systems Using a Support Vector Machine Classifier," Wireless Engineering and Technology, Vol. 4 No. 3, 2013, pp. 145-153. doi: 10.4236/wet.2013.43022.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] M. W. Aslam, Z. Zhu and A. K. Nandi, “Automatic Digital Modulation Classification Using Genetic Programming with K-Nearest Neighbor,” The 2010 Military Communications Conference—Unclassified Program Waveforms and Signal Processing Track.
[2] K. Hassan, I. Dayoub, W. Hamouda and M. Berbineau, “Automatic Modulation Recognition Using Wavelet Transform and Neural Networks in Wireless Systems,” EURASIP Journal on Advances in Signal Processing, 2010. doi:10.1155/2010/532898
[3] N. Yee, J. P. Linnartz and G. Fettweis, “Multi-Carrier CDMA in Indoor Wireless Radio Networks,” Proceedings PIMRC ‘93, Yokohama, 8-11 September 1993, pp. 109-113.
[4] G. Fettweis, “Multi-Carrier Code Division Multiple Access (MC-CDMA): Basic Idea,” Internal Report, Technekron Communication Systems.
[5] T. Zarey, J. Aboueiyy and K. N. Plataniotisyy, “KGDA for Signal Classification in Cognitive Radio.”
[6] M. Petrova, P. M¨ah¨onen and A. Osuna, “Multi-Class Classification of Analog and Digital Signals in Cognitive Radios Using Support Vector Machines,” Institute for Networked Systems, RWTH Aachen University Kackertstrasse 9, Aachen, Germany
[7] A. Ebrahimzadeh and G. Ardeshir, “A New Signal Type Classifier for Fading Environments,” Journal of Computing and Information Technology, Vol. 15, No. 3, 2007, pp. 257-266. doi:10.2498/cit.1000874
[8] A. Ebrahimzadeh and S. A. Seyedin, “Automatic Digital Modulation Identification in Dispersive Channels,” Proceedings of the 5th WSEAS International Conference on Telecommunications and Informatics, Istanbul, 27-29 May 2006, pp. 409-414.
[9] H. Hu, J. Song and Y. Wang, “Signal Classification Based on Spectral Correlation Analysis and SVM in Cognitive Radio,” 22nd International Conference on Advanced Information Networking and Applications, Okinawa, 25-28 March 2008.
[10] C.-S. Park, W. Jang and S. P. Nah, “Automatic Modulation Recognition Using Support Vector Machine in Software Radio Applications,” EW Lab, Agency for Defense Development, Korea.
[11] O. A. Dobre, A. Abdi, Y. Bar-Ness and W. Su, “Survey of Automatic Modulation Classfication Techniques Classical Approaches and New Trends,” Sarnoff Symposium, Princeton, 2005.
[12] T. Kinnunen, “Spectral Features for Automatic Test Independent Speaker Recognition,” Licentiate’s Thesis, Department of Computer Science, University of Joensuu, Finland, 2003.
[13] T. Matsui and S. Furui, “Comparison of Text-Independent Speaker Recognition Methods Using VQ-Distortion and Discrete/Continuous HMMs,” IEEE Transactions on Speech and Audio Processing, Vol. 2, No. 3, 1994, pp. 456-459. doi:10.1109/89.294363
[14] B. S. Andrew and C. Lindgren, “Speech Recognition Using Features Extracted from Phase Space Reconstructions,” 2003.
[15] J. S. Walker, “A Primer on Wavelets and Their Scientific Applications,” CRC Press LLC, 1999. doi:10.1201/9781420050011
[16] Z. Wu, X. Wang, Z. Gao and G. H. Ren, “Automatic Digital Modulation Recognition Based on Support Vector Machines.”
[17] J. C. Platt and N. Cristianini, “Large Margin DAGs for Multiclass Classification,” In: S. A. Solla, T. K. Leen and K.-R. M¨uller, Eds., MIT Press, Cambridge, 2000, pp. 547-553.

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