Wavelet network for nonlinearities reduction in multicarrier systems

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Resumen

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.

Idioma originalInglés
Título de la publicación alojadaNature Inspired Problem-Solving Methods in Knowledge Engineering - Second International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007, Proceedings
EditorialSpringer Verlag
Páginas28-36
Número de páginas9
EdiciónPART 2
ISBN (versión impresa)3540730540, 9783540730545
DOI
EstadoPublicada - 2007
Publicado de forma externa
Evento2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007 - La Manga del Mar Menor, Espana
Duración: 18 jun. 200721 jun. 2007

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NúmeroPART 2
Volumen4528 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia2nd International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2007
País/TerritorioEspana
CiudadLa Manga del Mar Menor
Período18/06/0721/06/07

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