Online adaptive nonlinear channel equalization using RBF neural networks

Tian Junxia; D. U. Liping; Kuang Jingming; Wang Hua

Online adaptive nonlinear channel equalization using RBF neural networks

Tian Junxia^*
, D. U. Liping
, Kuang Jingming
, Wang Hua

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Nonlinear distortions must be compensated in many real-life systems, which are encountered in digital satellite and microwave channels or others, so adaptive equalization is of considerable practical interest. Radial basis function neural networks have the ability to equalize nonlinear channels, and a simplified version of it is proposed to fit for online implement. We focus on stochastic gradient technique, and get a good performance by adjusting only one coefficient and one center closest to the input vector. Simulations are included to verify the algorithm.

Original language	English
Title of host publication	ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings
Editors	G. Benqing, X. Xiaowen
Pages	300-303
Number of pages	4
Publication status	Published - 2004
Event	ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications - Beijing, China Duration: 1 Nov 2004 → 4 Nov 2004

Publication series

Name	ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings

Conference

Conference	ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications
Country/Territory	China
City	Beijing
Period	1/11/04 → 4/11/04

Keywords

Adaptive equalization(ae)
Radical basis function(rbf)
Stochastic gradient(sg)

Cite this

Junxia, T., Liping, D. U., Jingming, K., & Hua, W. (2004). Online adaptive nonlinear channel equalization using RBF neural networks. In G. Benqing, & X. Xiaowen (Eds.), ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings (pp. 300-303). (ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings).

Junxia, Tian ; Liping, D. U. ; Jingming, Kuang et al. / Online adaptive nonlinear channel equalization using RBF neural networks. ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings. editor / G. Benqing ; X. Xiaowen. 2004. pp. 300-303 (ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings).

@inproceedings{378fd4b2d6344ba19d5c1c6372d54913,

title = "Online adaptive nonlinear channel equalization using RBF neural networks",

abstract = "Nonlinear distortions must be compensated in many real-life systems, which are encountered in digital satellite and microwave channels or others, so adaptive equalization is of considerable practical interest. Radial basis function neural networks have the ability to equalize nonlinear channels, and a simplified version of it is proposed to fit for online implement. We focus on stochastic gradient technique, and get a good performance by adjusting only one coefficient and one center closest to the input vector. Simulations are included to verify the algorithm.",

keywords = "Adaptive equalization(ae), Radical basis function(rbf), Stochastic gradient(sg)",

author = "Tian Junxia and Liping, \{D. U.\} and Kuang Jingming and Wang Hua",

year = "2004",

language = "English",

isbn = "0780385624",

series = "ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings",

pages = "300--303",

editor = "G. Benqing and X. Xiaowen",

booktitle = "ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings",

note = "ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications ; Conference date: 01-11-2004 Through 04-11-2004",

}

Junxia, T, Liping, DU, Jingming, K & Hua, W 2004, Online adaptive nonlinear channel equalization using RBF neural networks. in G Benqing & X Xiaowen (eds), ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings. ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings, pp. 300-303, ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Beijing, China, 1/11/04.

Online adaptive nonlinear channel equalization using RBF neural networks. / Junxia, Tian; Liping, D. U.; Jingming, Kuang et al.
ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings. ed. / G. Benqing; X. Xiaowen. 2004. p. 300-303 (ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Online adaptive nonlinear channel equalization using RBF neural networks

AU - Junxia, Tian

AU - Liping, D. U.

AU - Jingming, Kuang

AU - Hua, Wang

PY - 2004

Y1 - 2004

N2 - Nonlinear distortions must be compensated in many real-life systems, which are encountered in digital satellite and microwave channels or others, so adaptive equalization is of considerable practical interest. Radial basis function neural networks have the ability to equalize nonlinear channels, and a simplified version of it is proposed to fit for online implement. We focus on stochastic gradient technique, and get a good performance by adjusting only one coefficient and one center closest to the input vector. Simulations are included to verify the algorithm.

AB - Nonlinear distortions must be compensated in many real-life systems, which are encountered in digital satellite and microwave channels or others, so adaptive equalization is of considerable practical interest. Radial basis function neural networks have the ability to equalize nonlinear channels, and a simplified version of it is proposed to fit for online implement. We focus on stochastic gradient technique, and get a good performance by adjusting only one coefficient and one center closest to the input vector. Simulations are included to verify the algorithm.

KW - Adaptive equalization(ae)

KW - Radical basis function(rbf)

KW - Stochastic gradient(sg)

UR - https://www.scopus.com/pages/publications/20844438194

M3 - Conference contribution

AN - SCOPUS:20844438194

SN - 0780385624

T3 - ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings

SP - 300

EP - 303

BT - ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings

A2 - Benqing, G.

A2 - Xiaowen, X.

T2 - ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications

Y2 - 1 November 2004 through 4 November 2004

ER -

Junxia T, Liping DU, Jingming K, Hua W. Online adaptive nonlinear channel equalization using RBF neural networks. In Benqing G, Xiaowen X, editors, ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings. 2004. p. 300-303. (ICCEA 2004 - 2004 3rd International Conference on Computational Electromagnetics and its Applications, Proceedings).

Online adaptive nonlinear channel equalization using RBF neural networks

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Publication series

Conference

Keywords

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