Signal Classification Method Based on Support Vector Machine and High-Order Cumulants
ABSTRACT
In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.
In this paper, a classification method based on Support Vector Machine (SVM) is given in the digital modulation signal classification. The second, fourth and sixth order cumulants of the received signals are used as classification vectors firstly, then the kernel thought is used to map the feature vector to the high dimensional feature space and the optimum separating hyperplane is constructed in space to realize signal recognition. In order to build an effective and robust SVM classifier, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross- validation grid is adopted. Through the experiments it can be concluded that the classifier based on SVM has high performance and is more robust.
Cite this paper
nullX. ZHOU, Y. WU and B. YANG, "Signal Classification Method Based on Support Vector Machine and High-Order Cumulants," Wireless Sensor Network, Vol. 2 No. 1, 2010, pp. 48-52. doi: 10.4236/wsn.2010.21007.
nullX. ZHOU, Y. WU and B. YANG, "Signal Classification Method Based on Support Vector Machine and High-Order Cumulants," Wireless Sensor Network, Vol. 2 No. 1, 2010, pp. 48-52. doi: 10.4236/wsn.2010.21007.
References
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[1] K. Nandi and E. E. Azzouz, “Automatic modulation recognition [J],” Signal Processing, Vol. 46, No. 2, pp. 211– 222, 1995. [2] O. A. Dobre, A. Abdi, Y. Bar-Ness, et al., “Survey of automatic modulation classification techniques: Classical approaches and new trends [J],” IEE Communication, , Vol. 1, No. 2, pp. 137–156, 2007. [3] W. C. Han, H. Han, L. N. Wu, et al., “A 1-dimension structure adaptive self-organizing neural network for QAM signal classification [C],” Third International Conference on Natural Computation (ICNC 2007), HaiKou, August 24–27, 2007. [4] 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 [J],” Journal of Physics, International Symposium on Instrumentation Science and Technology, pp. 1264–1267, 2006. [5] H. Mustafa and M. Doroslovacki, “Digital modulation recognition using support vector machine classifier [C],” Proceedings of The Thirty-Eighth Asilomar Conference on Signals, Systems & Computers, November 2004. [6] O. A. Dobre, Y. B. Ness, and S. Wei, “Higher-order cyclic cumulants for high order modulation classification [C],” IEEE MILCOM, pp. 112–115, 2003. [7] Z. L. Wu, X. X. Wang, Z. Z. Gao, and G. H. Ren, “Automatic digital modulation recognition based on support vector machine [C],” IEEE Conference on Neural Networks and Brain, pp. 1025–1028, October 2005 [8] V. Vapnik, “Statistical learning theory [M],” Wiley, 1998. [9] B. Gou and X. W. Huang, “SVM multi-class classification [J],” Journal of Southern Yangtze University, Vol. 21, pp. 334–339, September 2006.