Reduced complexity implementation of Bayesian equaliser using local RBF network for channel equalisation problem

Author

Chng, Eng Siong ; Yang, H. ; Skarbek, W.

Author_Institution

Lab. for Artificial Brain Syst., LIKEN, Inst. of Phys. & Chem. Res., Saitama, Japan

Volume

32

Issue

1

fYear

1996

fDate

1/4/1996 12:00:00 AM

Firstpage

17

Lastpage

19

Abstract

The authors examine a method for reducing the implementation complexity of the RBF Bayesian equaliser using model selection. The selection process is based on finding a subset model to approximate the response of the full RBF model for the current input vector, and not for the entire input space. Using such a scheme, for cases in which the channel equalisation problem is non-stationary, the requirement for updating all the centre locations is removed, and the implementation complexity is reduced. Using computer simulations, we show that the number of centres can be greatly reduced without compromising classification performance

Keywords

Bayes methods; digital communication; equalisers; error statistics; probability; BER performance; Bayesian equaliser; channel equalisation problem; local RBF network; model selection; radial basis function; reduced complexity implementation; subset model;

fLanguage

English

Journal_Title

Electronics Letters

Publisher

iet

ISSN

0013-5194

Type

jour

DOI

10.1049/el:19960009

Filename

490701