Title :
Blind equalization using higher order statistics and neural networks: a comparison between multilayer feedforward network and radial basis function network
Author :
Rui, Li ; Saratchandran, P. ; Sundararajan, N.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
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
Keywords :
backpropagation; blind equalisers; convergence; higher order statistics; multilayer perceptrons; parameter estimation; radial basis function networks; signal processing; backpropagation algorithm; blind equalization; channel estimation; convergence; higher order statistics; minimal resource allocation network; multilayer feedforward network; neural network equalizers; non-minimum phase systems; performance; radial basis function network; signal recovery; Backpropagation algorithms; Blind equalizers; Communication channels; Digital communication; Feedforward neural networks; Higher order statistics; Multi-layer neural network; Neural networks; Neurons; Radial basis function networks;
Conference_Titel :
Higher-Order Statistics, 1999. Proceedings of the IEEE Signal Processing Workshop on
Conference_Location :
Caesarea
Print_ISBN :
0-7695-0140-0
DOI :
10.1109/HOST.1999.778700
