Presented at the
1999 IEEE Int.
Symposium on Circuits and Systems
Channel Equalization by Feedforward Neural Networks
Biao Lu and
Brian L. Evans
Embedded Signal Processing Laboratory,
Department of Electrical and Computer Engineering,
Engineering Science Building,
The University of Texas at Austin,
Austin, TX 78712-1084 USA
A signal suffers from nonlinear, linear, and additive
distortion when transmitted through a channel.
Linear equalizers are commonly used in receivers to compensate for
linear channel distortion.
As an alternative, nonlinear equalizers have the potential to compensate
for all three sources of channel distortion.
Previous authors have shown that nonlinear feedforward equalizers
based on either multilayer perceptron (MLP) or radial basis function
(RBF) neural networks can outperform linear equalizers.
In this paper, we compare the performance of MLP vs. RBF equalizers
in terms of symbol error rate vs. SNR.
We design a reduced complexity neural network equalizer by cascading
an MLP and a RBF network.
In simulation, the new MLP-RBF equalizer outperforms MLP equalizers
and RBF equalizers.
The paper is available in
Last Updated 11/08/04.