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Fuzzy-ARTMAP based Multi-User Detection
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 Title & Authors
Fuzzy-ARTMAP based Multi-User Detection
Lee, Jung-Sik;
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 Abstract
This paper studies the application of a fuzzy-ARTMAP (FAM) neural network to multi-user detector (MUD) for direct sequence (DS)-code division multiple access (CDMA) system. This method shows new solution for solving the problems, such as complexity and long training, which is found when implementing the previously developed neural-basis MUDs. The proposed FAM based MUD 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 capabilities 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 FAM based MUD is compared with other neural net based MUDs in terms of the bit error rate.
 Keywords
Multiuser;Detector;Fuzzy-ARTMAP;Neural Network;DS-CDMA;
 Language
English
 Cited by
1.
가변 학습을 적용한 퍼지 ART 신경망의 패턴 인식 능력 향상,이창주;손병희;홍희식;

한국통신학회논문지, 2013. vol.38B. 12, pp.954-961 crossref(new window)
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