On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels. Issue 7 (1st May 2016)

• Record Type: Journal Article
• Authors: Kharbech, Sofiane; Dayoub, Iyad; Zwingelstein‐Colin, Marie; Simon, Eric Pierre
• Is Part Of: IET communications. Volume 10:Issue 7(2016)
• Page Start: 790
• Page End: 795
• Publication Date: 2016-05-01
• Subjects: cognitive radio -- wireless channels -- MIMO communication -- statistical analysis; blind feature classifiers -- automatic modulation classification -- multiple‐input‐multiple‐output channels -- modulation recognition -- environmental awareness -- cognitive radio systems -- feature based automatic modulation classification algorithms -- FB‐AMC algorithms -- artificial neural networks -- K‐nearest neighbours -- support vector machines -- statistical pattern recognition techniques -- computational complexity -- optimal model; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2015.1124
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : 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.