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

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

doi:10.1109/IJCNN.2004.1380939

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

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

Department of Computer Science and Information Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages	2089-2094
Number of pages	6
Volume	3
DOIs	https://doi.org/10.1109/IJCNN.2004.1380939
Publication status	Published - 2004 Dec 1
Event	2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 2004 Jul 25 → 2004 Jul 29

Other

Other	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country	Hungary
City	Budapest
Period	04-07-25 → 04-07-29

All Science Journal Classification (ASJC) codes

Software

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10.1109/IJCNN.2004.1380939

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Cite this

@inproceedings{f5872bc40bae4933b4bc4aef0b0d4f60,

title = "A nonlinear time-varying channel equalizer using self-organizing wavelet neural networks",

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.",

author = "Lin, {Cheng Jian} and Shih, {Chuan Chan} and Po-Yueh Chen",

year = "2004",

month = dec,

day = "1",

doi = "10.1109/IJCNN.2004.1380939",

language = "English",

isbn = "0780383591",

volume = "3",

pages = "2089--2094",

booktitle = "2004 IEEE International Joint Conference on Neural Networks - Proceedings",

note = "2004 IEEE International Joint Conference on Neural Networks - Proceedings ; Conference date: 25-07-2004 Through 29-07-2004",

}

Lin, CJ, Shih, CC & 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, 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Budapest, Hungary, 04-07-25. https://doi.org/10.1109/IJCNN.2004.1380939

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AU - Lin, Cheng Jian

AU - Shih, Chuan Chan

AU - Chen, Po-Yueh

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Y1 - 2004/12/1

N2 - 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.

AB - 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.

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BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings

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