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

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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2004 IEEE International Joint Conference on Neural Networks - Proceedings
頁面2089-2094
頁數6
3
DOIs
出版狀態Published - 2004 十二月 1
事件2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary
持續時間: 2004 七月 252004 七月 29

Other

Other2004 IEEE International Joint Conference on Neural Networks - Proceedings
國家Hungary
城市Budapest
期間04-07-2504-07-29

All Science Journal Classification (ASJC) codes

  • Software

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  • 引用此

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