Neural network equalizer

Chulhee Lee, Jinwook Go, Byungjoon Baek, Hyunsoo Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we view equalization as a multi-class classification problem and use neural networks to detect binary signals in the presence of noise and interference. In particular, we compare the performance of a recently published training algorithm, a multi-gradient, with that of the conventional back-propagation. Then, we apply a feature extraction to obtain more efficient neural networks. Experiments show that neural network equalizers which view equalization as multi-class problems provide significantly improved performance compared to the conventional LMS algorithm while the decision boundary feature extraction method significantly reduces the complexity of the network.

Original languageEnglish
Title of host publicationInternational Conference on Intelligent Computing, ICIC 2006, Proceedings
PublisherSpringer Verlag
Pages204-215
Number of pages12
ISBN (Print)3540372717, 9783540372714
DOIs
Publication statusPublished - 2006 Jan 1
EventInternational Conference on Intelligent Computing, ICIC 2006 - Kunming, China
Duration: 2006 Aug 162006 Aug 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4113 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Conference on Intelligent Computing, ICIC 2006
CountryChina
CityKunming
Period06/8/1606/8/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lee, C., Go, J., Baek, B., & Choi, H. (2006). Neural network equalizer. In International Conference on Intelligent Computing, ICIC 2006, Proceedings (pp. 204-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4113 LNCS - I). Springer Verlag. https://doi.org/10.1007/11816157_20