Automatic Modulation Classification Using Moments and Likelihood Maximization - 2018


Motivated by the fact that moments of the received signal are easy to compute and can give a simple means to automatically classify the modulation of the transmitted signal, we propose a hybrid method for automatic modulation classification that lies in the intersection between chance-based mostly and feature-based classifiers. Specifically, the proposed technique depends on statistical moments along with a most probability engine. We show that the proposed method offers a good trade-off between classification accuracy and complexity relative to the maximum probability classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-primarily based K-nearest neighbor classifiers, the linear support vector machine classifier and also the fold-based Kolmogorov-Smirnov algorithm.

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