Learning algorithms for complex-valued neural networks in communication signal processing and adaptive equalization as its application

Cheolwoo You, Daesik Hong

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In this chapter, the complex Backpropagation (BP) algorithm for the complex backpropagation neural networks (BPN) consisting of the suitable node activation functions having multi-saturated output regions is presented and analyzed by the benchmark testing. And then the complex BPN is utilized as nonlinear adaptive equalizers that can deal with both quadrature amplitude modulation (QAM) and phase shift key (PSK) signals of any constellation sizes. In addition, four nonlinear blind equalization schemes using complex BPN for M-ary QAM signals are described and their learning algorithms are presented. The presented complex BP equalizer (CBPE) gives, compared with conventional linear complex equalizers, an outstanding improvement with respect to bit error rate (BER) when channel distortions are nonlinear.

Original languageEnglish
Title of host publicationComplex-Valued Neural Networks
Subtitle of host publicationUtilizing High-Dimensional Parameters
PublisherIGI Global
Pages194-235
Number of pages42
ISBN (Print)9781605662145
DOIs
Publication statusPublished - 2009

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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