Online adaptive nonlinear channel equalization using RBF neural networks

被引：0

作者：

Tian, JX ^{[1
]}

Du, LP ^{[1
]}

Kuang, JM ^{[1
]}

Wang, H ^{[1
]}

机构：

[1] Beijing Inst Technol, Sch Informat Sci & Technol, Dept Elect Engn, Beijing 100081, Peoples R China

来源：

2004 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL ELECTROMAGNETICS AND ITS APPLICATIONS, PROCEEDINGS | 2004年

关键词：

radical basis function(RBF); adaptive equalization(AE); stochastic gradient(SG);

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：300 / 303

页数：4

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