Blind digital modulation identification for time-selective MIMO channels

Kharbech, Sofiane; Dayoub, Iyad; Zwingelstein, Marie; Simon, Eric; Hassan, Kais

Document type :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1109/LWC.2014.2318041

Title :

Blind digital modulation identification for time-selective MIMO channels

Author(s) :

Kharbech, Sofiane [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Dayoub, Iyad [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Zwingelstein, Marie [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Simon, Eric [Auteur]

Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Hassan, Kais [Auteur]
Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance [Lab-STICC]

Journal title :

IEEE Wireless Communications Letters

Pages :

373-376

Publisher :

IEEE comsoc

Publication date :

2014

ISSN :

2162-2337

HAL domain(s) :

Sciences de l'ingénieur [physics]

English abstract : [en]

This paper addresses the problem of blind digital modulation identification in time-selective Multiple-Input Multiple-Output channels. Our objective is to recognize modulation schemes in highly-mobile communication ...
Show more >This paper addresses the problem of blind digital modulation identification in time-selective Multiple-Input Multiple-Output channels. Our objective is to recognize modulation schemes in highly-mobile communication environments, for military or high-speed railway applications, without signal knowledge or Channel State Information at the receiver. The proposed identification process is based on Blind Source Separation (BSS) and feature classification. We introduce a sliding window technique for the BSS of a faded-mixture to overcome the effect of the high mobility. Then, to improve the recognition of modulation schemes, we adopt a specific multi Artificial- Neural-Network (ANN) classifier, where each ANN is trained to be used within a particular Signal-to-Noise Ratio range. The proposed identifier has a good probability for achieving correct identifications under high velocity for typical carrier frequency and bandwidth.Show less >

Language :

Anglais

Popular science :

Non

Collections :

Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL

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