Channel equalization using radial basis function network

Authors:
J. Lee;C. D. Beach;N. Tepedelenlioglu
Affiliations:
Dept. of Electr. & Comput. Eng., Florida Inst. of Technol., Melbourne, FL, USA;-;-
Venue:
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 03
Year:
1996

Citing 0
Cited 6

A Hybrid Genetic Algorithm with Simulated Annealing for Nonlinear Blind Equalization Using RBF Networks

ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Equalization of Channel Distortion Using Nonlinear Neuro-Fuzzy Network

ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Reduced RBF centers based multi-user detection in DS-CDMA systems

ICHIT'06 Proceedings of the 1st international conference on Advances in hybrid information technology
A division algebraic framework for multidimensional support vector regression

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Unscented kalman filter-trained MRAN equalizer for nonlinear channels

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Using unscented kalman filter for training the minimal resource allocation neural network

ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss the application of a radial basis function (RBF) network to the channel equalization problem. In particular, the purpose of the paper is to improve the previously developed RBF equalizer with training using K-means and LMS methods; reducing the RBF network size by considering a lesser number of RBF centers, and developing new techniques for determining channel order which is required to specify the structure of an RBF equalizer. A linear regression model was used for estimating the channel order. The basic idea of reducing the network size is to select the centers, based on the channel lag. This work includes the comparison of the limits of mean square error (MSE) convergence of both a linear equalizer and an RBF equalizer.