Channel Equalization Using Fuzzy-ARTMAP

Jungsik LEE  Yeonsung CHOI  Jaewan LEE  Soowhan HAN  

Publication
IEICE TRANSACTIONS on Communications   Vol.E85-B   No.4   pp.826-830
Publication Date: 2002/04/01
Online ISSN: 
DOI: 
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Category: Communication Devices/Circuits
Keyword: 
channel equalizer,  neural network,  fuzzy ARTMAP,  

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Summary: 
This paper discusses the application of a fuzzy-ARTMAP neural network to digital communications channel equalization. This approach provides new solutions for solving the problems, such as complexity and long training, which found when implementing the previously developed neural-basis equalizers. The proposed fuzzy-ARTMAP equalizer is fast and easy to train and includes capabilities not found in other neural network approaches; a small number of parameters, no requirements for the choice of initial weights, automatic increase of hidden units, no risk of getting trapped in local minima, and the capability of adding new data without retraining previously trained data. In simulation studies, binary signals were generated at random in a linear channel with Gaussian noise. The performance of the proposed equalizer is compared with other neural net basis equalizers, specifically MLP and RBF equalizers.