Blind equalization using higher order statistics and neural networks: a comparison between multilayer feedforward network and radial basis function network

This paper compares two methods of blind equalization for non-minimum phase systems. Higher order statistics is used to estimate the unknown channel and neural networks are used as equalizers to recover the transmitted signal. Two kinds of neural network equalizers are realized; one is the multilayer feedforward network (MFN) based on the backpropagation algorithm, the other is the minimal resource allocation network (MRAN) which is a radial basis function network. It is shown that the MRAN equalizer performs better and converges faster than MFN equalizer.