On classifiers for blind feature‐based ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels
Author(s) :
Kharbech, Sofiane [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Dayoub, Iyad [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Zwingelstein, Marie [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Simon, Eric [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Dayoub, Iyad [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Zwingelstein, Marie [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Simon, Eric [Auteur]
Journal title :
IET Communications
Pages :
790-795
Publisher :
Institution of Engineering and Technology
Publication date :
2016-05
ISSN :
1751-8628
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
Informatique [cs]/Réseaux et télécommunications [cs.NI]
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Sciences de l'ingénieur [physics]/Electronique
English abstract : [en]
Modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based ...
Show more >Modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based automatic modulation classification (FB-AMC) algorithms. Classifiers whose models will be designed are classification tree, K-nearest neighbours, artificial neural networks (ANNs), and support vector machines. In this study, they apply some statistical pattern recognition techniques in the context of blind FB-AMC over multiple-input–multiple-output channels. Comparison criteria are classification accuracy and computational complexity. To improve the impartiality of this comparison, each classifier is optimally deployed by selecting its optimal model with respect to their context. Model selection for the classifiers is done using the ‘k-fold cross-validation’ model validation technique. The comparison study, within the considered context, shows that ANN classifiers have the best performance/complexity tradeoff.Show less >
Show more >Modulation recognition is crucial for a good environmental awareness required by cognitive radio systems. In this study, the authors design and compare models of four among the most commonly used classifiers for feature-based automatic modulation classification (FB-AMC) algorithms. Classifiers whose models will be designed are classification tree, K-nearest neighbours, artificial neural networks (ANNs), and support vector machines. In this study, they apply some statistical pattern recognition techniques in the context of blind FB-AMC over multiple-input–multiple-output channels. Comparison criteria are classification accuracy and computational complexity. To improve the impartiality of this comparison, each classifier is optimally deployed by selecting its optimal model with respect to their context. Model selection for the classifiers is done using the ‘k-fold cross-validation’ model validation technique. The comparison study, within the considered context, shows that ANN classifiers have the best performance/complexity tradeoff.Show less >
Language :
Anglais
Popular science :
Non
Source :