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Automatic Modulation Classification Using Moments and Likelihood Maximization
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DOI：10.1109/LCOMM.2018.2806489.png)
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Abstract
En 中文
Motivated by the fact that moments of the received signal are easy to compute and can provide a simple way to automatically classify the modulation of the transmitted signal, we propose a hybrid method for automatic modulation classification that lies in the intersection between likelihood-based and feature-based classifiers. Specifically, the proposed method relies on statistical moments along with a maximum likelihood engine. We show that the proposed method offers a good trade-off between classification accuracy and complexity relative to the maximum likelihood classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbor classifiers, the linear support vector machine classifier and the fold-based Kolmogorov-Smirnov algorithm.
Keywords:
Automatic modulation classification
support vector machines
genetic programming
machine learning
moments-based classification
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