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Digital Modulation Types Recognition using HOS and WT in Multipath Fading Environments  

Park, Cheol-Sun (Agency for Defense Development)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.45, no.5, 2008 , pp. 102-109 More about this Journal
Abstract
In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.
Keywords
Modulation Classification (MC); High-Order Statistics (HOS); Wavelet Transformation (WT); Support Vector Machine(SVM);
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1 G. Hatzichristos, and M. P. Fargues, " A hierarchical approach to the classification of digital modulation types in multipath environment," in Proc. Asilmar Conf. on Signals, Systems and Coputers, 2001, pp.1494-1498
2 John C. Platt, Nello Cristianini, and John Shawe-Taylor, "Large Margin DAGs for Multiclass Classification," in Advances in Neural Information Processing System, MIT Press, 2000, pp.547-553
3 Signal Processing Information Base, Rice University, Houston, TX, http://spib.ece.rice.edu /spib/data/signals
4 Ataollah Ebrahimzadeh, and Gholam Reza Ardeshir, "A New Signal Type Classifier for Fading Environments," Journal of Computing and Information Technology, CIT 15, 2007, pp.257-266
5 Cheol-Sun Park, Jun-Ho Choi, Sun-Phil Nah, Won Jang, and Dae Young Kim, "Automatic Modulation Recognition of Digital Signals using Wavelet Features and SVM," in Proc. 10th IEEE ICACT, Feb. 2008, pp.387-390
6 X. Z. Feng, J Yang, F. L. Luo, J. Y. Chen, and X. P. Zhong, "Automatic Modulation Recognition by Support Vector Machines using Wavelet kernel," Journal of Physics: Int. Symp. On Instrumentation Science and Technology, 2006, pp.1264-1267
7 K. C. Ho, W. Prokopiw, and Y. T. Chan, "Modulation Identification by the Wavelet Transform," in Proc. IEEE MILCOM, Nov. 1995, pp.886-890