Channel equalization by feedforward neural networks

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Channel equalization by feedforward neural networks
A signal su ers 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.
Biao Lu, Brian L. Evans
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Authors Biao Lu, Brian L. Evans
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