Identification of fifth-order Wiener and Hammerstein channels based on the estimation of an associated Volterra kernel

Zouhour Ben Ahmed 1 Gérard Favier 2 Nabil Derbel 1
1 CEM Lab - ENIS - Département de Génie Électrique de Sfax [ENIS]
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe SIGNAL
SIS - Signal, Images et Systèmes
Abstract : In this paper, we consider the problem of identification of fifth-order Wiener and Hammerstein nonlinear communication channels using the estimation of an associated Volterra kernel. We exploit the special form of the fifth-order associated Volterra kernel for deriving two algorithms that allow to estimate the parameters of the linear part of these channels. In the case of a Wiener channel, the associated Volterra kernel is a tensor satisfying a rank-one PARAFAC decomposition whose parameters can be estimated by means of an alternating least squares (ALS) algorithm. In the case of a Hammerstein channel, its associated Volterra kernel is a diagonal tensor, which leads to a closed-form solution for estimating the parameters of the linear block. The coefficients of the nonlinear block modeled as a fifth degree polynomial are then estimated by means of the standard non recursive least squares (LS) algorithm. The performance of the proposed identification methods is illustrated by means of Monte Carlo simulation results.
Type de document :
Article dans une revue
International Journal of Computer Applications, IJCA, 2015, 123 (7), pp.1-5. 〈10.5120/ijca2015905405〉
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-01246201
Contributeur : Gérard Favier <>
Soumis le : vendredi 18 décembre 2015 - 11:23:38
Dernière modification le : samedi 19 décembre 2015 - 01:05:12

Identifiants

Collections

UNICE | I3S

Citation

Zouhour Ben Ahmed, Gérard Favier, Nabil Derbel. Identification of fifth-order Wiener and Hammerstein channels based on the estimation of an associated Volterra kernel . International Journal of Computer Applications, IJCA, 2015, 123 (7), pp.1-5. 〈10.5120/ijca2015905405〉. 〈hal-01246201〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

44

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD