Wavelet network for nonlinearities reduction in multicarrier systems

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Abstract

In this paper, we propose a wavelet neural network suitable for reducing nonlinear distortion introduced by a traveling wave tube amplifier (TWTA) over multicarrier systems. Parameters of the proposed network are identified using an hybrid training algorithm, which adapts the linear output parameters using the least square algorithm and the nonlinear parameters of the hidden nodes are trained using the gradient descent algorithm. Computer simulation results confirm that the proposed wavelet network achieves a bit error rate performance very close to the ideal case of linear amplification.

Original languageEnglish
Title of host publicationNature Inspired Problem-Solving Methods in Knowledge Engineering - Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, Proceedings
Pages28-36
Number of pages9
EditionPART 2
StatePublished - 1 Dec 2007
Event2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007 - La Manga del Mar Menor, Spain
Duration: 18 Jun 200721 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4528 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007
CountrySpain
CityLa Manga del Mar Menor
Period18/06/0721/06/07

Keywords

  • Linearizing
  • Multicarrier
  • Wavelet network

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