[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.