Blind modulation identification for MIMO systems
Type de document :
Communication dans un congrès avec actes
Titre :
Blind modulation identification for MIMO systems
Auteur(s) :
Hassan, Kais [Auteur]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Nsiala-Nzéza, Crépin [Auteur]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Berbineau, Marion [Auteur]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Hamouda, Walaa [Auteur]
Concordia University [Montreal]
Dayoub, Iyad [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Nsiala-Nzéza, Crépin [Auteur]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Berbineau, Marion [Auteur]
Laboratoire Electronique, Ondes et Signaux pour les Transports [INRETS/LEOST]
Hamouda, Walaa [Auteur]
Concordia University [Montreal]
Dayoub, Iyad [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Titre de la manifestation scientifique :
IEEE Global Telecommunications Conference, GLOBECOM 2010
Ville :
Miami, FL
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2010-12-06
Titre de l’ouvrage :
Proceedings of 2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
Éditeur :
_
Date de publication :
2010
Mot(s)-clé(s) en anglais :
MIMO
Feature extraction
Signal to noise ratio
Artificial neural networks
Channel estimation
Digital modulation
Feature extraction
Signal to noise ratio
Artificial neural networks
Channel estimation
Digital modulation
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output ...
Lire la suite >Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output (MIMO) systems is proposed. The suggested algorithm is verified using higher order statistical moments and cumulants of the received signal. A multi-layer neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying linear modulation types and the modulation order without any priori signal information. This study covers different MIMO systems with and without channel state information (CSI). The proposed classifier is evaluated through the probability of identification where we show that our proposed algorithm is capable of identifying the modulation scheme with high accuracy in excellent signal-to-noise ratio (SNR) range.Lire moins >
Lire la suite >Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output (MIMO) systems is proposed. The suggested algorithm is verified using higher order statistical moments and cumulants of the received signal. A multi-layer neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying linear modulation types and the modulation order without any priori signal information. This study covers different MIMO systems with and without channel state information (CSI). The proposed classifier is evaluated through the probability of identification where we show that our proposed algorithm is capable of identifying the modulation scheme with high accuracy in excellent signal-to-noise ratio (SNR) range.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Source :