论文信息 - Blind Neural Equalizer using Higher-Order Statistics

Blind Neural Equalizer using Higher-Order Statistics

This paper discusses a blind equalization technique for FIR channel system, that might be minimum phase or not, in digital communication. The proposed techniques consist of two parts. One is to estimate the original channel coefficients based on fourth-order cumulants of the channel output, the other is to employ RBF neural network to model an inverse system fur the original channel. Here, the estimated channel is used as a reference system to train the RBF. The proposed RBF equalizer provides fast and easy teaming, due to the structural efficiency and excellent recognition-capability of R3F neural network. Throughout the simulation studies, it was found that the proposed blind RBF equalizer performed favorably better than the blind MLP equalizer, while requiring the relatively smaller computation steps in tranining.

Jung-sik Lee

[1] Bahram Shafai,et al. Blind equalization using higher order cumulants and neural network , 1994, IEEE Trans. Signal Process..

[2] Doo-Ywan Kim,et al. Image Recognition by Learning Multi-Valued Logic Neural Network , 2002, Int. J. Fuzzy Log. Intell. Syst..

[3] Sheng Chen,et al. A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[4] Fang-Biau Ueng,et al. Adaptive Blind Equalization Using Second- and Higher Order Statistics , 1995, IEEE J. Sel. Areas Commun..

[5] C.F.N. Cowan,et al. Adaptive equalization of finite nonlinear channels using multilayer perceptron , 1990 .

[6] Shang-Liang Chen,et al. Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[7] Keon-Myung Lee. Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies , 2002, Int. J. Fuzzy Log. Intell. Syst..