Automatic Modulation Classification Using Moments And Likelihood Maximization

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@Article{Abu-Romoh:2018:ieeeCL,

• author = "M. Abu-Romoh and A. Aboutaleb and Z. Rezki",
• journal = "IEEE Communications Letters",
• title = "Automatic Modulation Classification Using Moments And Likelihood Maximization",
• year = "2018",
• abstract = "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 tradeoff between classification accuracy and complexity relative to the Maximum Likelihood (ML) classifier. Furthermore, our classifier outperforms state-of-the-art machine learning classifiers, such as genetic programming-based K-nearest neighbour (GP-KNN) classifiers, the linear support vector machine classifier (LSVM) and the fold-based Kolmogorov-Smirnov (FB-KS) algorithm.",
• keywords = "genetic algorithms, genetic programming, Feature extraction, Machine learning algorithms, Modulation, Probability density function, Receivers, Signal to noise ratio, Support vector machines",
• DOI = "doi:10.1109/LCOMM.2018.2806489",
• ISSN = "1089-7798",
• notes = "Also known as \cite{8292836}",

}

Genetic Programming entries for M Abu-Romoh A Aboutaleb Z Rezki

Citations