Low Complexity Automatic Modulation Classification Based on Order Statistics

Abstract

In this paper, we propose two low-complexity automatic modulation classification (AMC) classifiers based on order-statistics: the linear support vector machine (LSVM) and the approximate maximum likelihood (AML). Specifically, LSVM applies the linear combination of the entire order-statistics of the received signals for the classification, while AML resorts to the asymptotic distribution of the reduced order- statistics to decrease the computational complexity. The Simulations show that the performance of our proposed classifiers is close to that of the maximum likelihood (ML) classifier and outperforms the Kolmogorov-Smirnov (KS) and cumulant-based classifiers. While the complexity of our proposed classifiers is much lower than that of the ML classifier.