Blind channel equalization based on Complex-valued neural network and probability density fitting - Equipe Communication System Design Accéder directement au contenu
Communication Dans Un Congrès Année : 2022

Blind channel equalization based on Complex-valued neural network and probability density fitting

Résumé

In this paper, we study blind equalization techniques to reduce the intersymbol interference (ISI) and we are particularly interested in equalizers based on probability density fitting (PDF). The PDF criterion was used with conventional linear equalizers. So we try in this paper to use this criterion in a nonlinear context using a neural network architecture. The network weights are updated by minimizing, at first, the stochastic quadratic distance, then the Multimodulus quadratic distance between the equalized PDF and some target distribution. Our approach shows a better performance in terms of mean square error (MSE) and symbol error rate (SER).
Fichier principal
Vignette du fichier
IWCMC2022_Blind_Channel_equalization.pdf (259.57 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03633511 , version 1 (07-04-2022)

Licence

Copyright (Tous droits réservés)

Identifiants

Citer

Chouaib Farhati, Souhaila Fki, Abdeldjalil Aissa El Bey, Fatma Abdelkefi. Blind channel equalization based on Complex-valued neural network and probability density fitting. IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), May 2022, Dubrovnik, Croatia. ⟨10.1109/IWCMC55113.2022.9824100⟩. ⟨hal-03633511⟩
81 Consultations
62 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More