A nonlinear time-varying channel equalizer using self-organizing wavelet neural networks

Cheng Jian Lin, Chuan Chan Shih, Po-Yueh Chen

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

1 Citation (Scopus)

Abstract

This paper describes the self-organizing wavelet neural network (SOWNN) for nonlinear time-varying channel equalizers. The SOWNN model has a four-layer structure which is comprised of an input layer, a wavelet layer, a product layer and an output layer. The derivative online learning algorithm involves two kinds of learning. The structure learning is performed to determine the network structure and the parameter learning is to adjust the shape of the wavelet bases and the connection weights of a SOWNN. The proposed equalizer is enhanced in order to handle the highly nonlinear functionality. Computer simulation results show that the bit error rate of the SOWNN equalizer is very close to that of the optimal equalizer.

Original languageEnglish
Title of host publication2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages2089-2094
Number of pages6
Volume3
DOIs
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
Duration: 2004 Jul 252004 Jul 29

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
CountryHungary
CityBudapest
Period04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Lin, C. J., Shih, C. C., & Chen, P-Y. (2004). A nonlinear time-varying channel equalizer using self-organizing wavelet neural networks. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (Vol. 3, pp. 2089-2094) https://doi.org/10.1109/IJCNN.2004.1380939