Welcome to the InfoSci Platform
Complex-Valued Neural Networks for Equalization of Communication Channels

Complex-Valued Neural Networks for Equalization of Communication Channels

Rajoo Pandey
Copyright: © 2009 |Pages: 26
ISBN13: 9781605662145|ISBN10: 1605662143|ISBN13 Softcover: 9781616925628|EISBN13: 9781605662152
DOI: 10.4018/978-1-60566-214-5.ch008
Cite Chapter Cite Chapter

MLA

Pandey, Rajoo. "Complex-Valued Neural Networks for Equalization of Communication Channels." Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, IGI Global, 2009, pp. 168-193. https://doi.org/10.4018/978-1-60566-214-5.ch008

APA

Pandey, R. (2009). Complex-Valued Neural Networks for Equalization of Communication Channels. In T. Nitta (Ed.), Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters (pp. 168-193). IGI Global. https://doi.org/10.4018/978-1-60566-214-5.ch008

Chicago

Pandey, Rajoo. "Complex-Valued Neural Networks for Equalization of Communication Channels." In Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters, edited by Tohru Nitta, 168-193. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-214-5.ch008

Export Reference

Mendeley
Favorite

Abstract

The equalization of digital communication channel is an important task in high speed data transmission techniques. The multipath channels cause the transmitted symbols to spread and overlap over successive time intervals. The distortion caused by this problem is called inter-symbol interference (ISI) and is required to be removed for reliable communication of data over communication channels. In this task of ISI removal, the signals are complex-valued and processing has to be done in a complex multidimensional space. The growing interest in complex-valued neural networks has spurred the development of many new algorithms for equalization of communication channels in the recent past. This chapter illustrates the application of various types of complex-valued neural networks such as radial basis function networks (RBFN), multilayer feedforward networks and recurrent neural networks for training sequence-based as well as blind equalization of communication channels. The structures and algorithms for these equalizers are presented and performances based on simulation studies are analyzed highlighting their advantages and the important issues involved.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.