Blind channel equalization based on Complex-valued neural network and probability density fitting - Archive ouverte HAL Access content directly
Conference Papers Year :

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

(1) , (1) , (2, 3) , (1)
1
2
3

Abstract

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
Origin : Files produced by the author(s)

Dates and versions

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

Licence

Copyright

Identifiers

  • HAL Id : hal-03633511 , version 1

Cite

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. ⟨hal-03633511⟩
58 View
7 Download

Share

Gmail Facebook Twitter LinkedIn More