Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization

Authors:
Haiquan Zhao;Jiashu Zhang
Affiliations:
Si-Chuan province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China and Department of Electronic Engineering, Chengdu University of Information Tec ...;Si-Chuan province Key Lab of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031, China
Venue:
Signal Processing
Year:
2008

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Abstract

Nonlinear intersymbol interference (ISI) leads to significant error rate in nonlinear communication and digital storage channel. In this paper, therefore, a novel computationally efficient functional link neural network cascaded with Chebyshev orthogonal polynomial is proposed to combat nonlinear ISI. The equalizer has a simple structure in which the nonlinearity is introduced by functional expansion of the input pattern by trigonometric polynomial and Chebyshev orthogonal polynomial. Due to the input pattern and nonlinear approximation enhancement, the proposed structure can approximate arbitrarily nonlinear decision boundaries. It has been utilized for nonlinear channel equalization. The performance of the proposed adaptive nonlinear equalizer is compared with functional link neural network (FLNN) equalizer, multilayer perceptron (MLP) network and radial basis function (RBF) along with conventional normalized least-mean-square algorithms (NLMS) for different linear and nonlinear channel models. The comparison of convergence rate, bit error rate (BER) and steady state error performance, and computational complexity involved for neural network equalizers is provided.