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 | |
| 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 |
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A nonlinear time-varying channel equalizer using self-organizing wavelet neural networks. / Lin, Cheng Jian; Shih, Chuan Chan; Chen, Po-Yueh.
2004 IEEE International Joint Conference on Neural Networks - Proceedings. Vol. 3 2004. p. 2089-2094.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A nonlinear time-varying channel equalizer using self-organizing wavelet neural networks
AU - Lin, Cheng Jian
AU - Shih, Chuan Chan
AU - Chen, Po-Yueh
PY - 2004/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=10844227342&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=10844227342&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2004.1380939
DO - 10.1109/IJCNN.2004.1380939
M3 - Conference contribution
SN - 0780383591
VL - 3
SP - 2089
EP - 2094
BT - 2004 IEEE International Joint Conference on Neural Networks - Proceedings
ER -