On classifiers for blind feature-based automatic modulation classification over multiple-input–multiple-output channels


Modulation recognition is crucial for a good environmental awareness needed by cognitive radio systems. In this study, the authors design and compare models of 4 among the most commonly used classifiers for feature-based mostly 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 choosing its optimal model with respect to their context. Model choice for the classifiers is finished using the 'k-fold cross-validation’ model validation technique. The comparison study, inside the considered context, shows that ANN classifiers have the simplest performance/complexity tradeoff.

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