Hierarchical Classification of Analog and Digital Modulation Schemes using Higher-Order Statistics and Support Vector Machines

Abstract Automatic Modulation Classification (AMC) algorithms play an important role in various military and civilian applications. There have been numerous AMC algorithms reported in the literature, most of which focus on synthetic signals with a limited number of modulation types having distinctive constellations. The efficient classification of high-order modulation schemes under real propagation effects using models with low complexity is still a challenge. In this paper, employing quadratic SVM, a feature-based (FB) hierarchical classification method is proposed to accurately classify especially higher-order modulation schemes and its performance is investigated using over the air (OTA) collected data. Statistical features, higher-order moments, and higher-order cumulants are employed as features. Then, the performances of some well-known classifiers are evaluated, and the classifier presenting the best performance is employed in the proposed hierarchical classification model. An OTA dataset containing 17 analog and digital modulation schemes is used to evaluate the performance of the proposed classification model. With the proposed hierarchical classification algorithm, a significant improvement has been achieved, especially in higher-order modulation schemes. The overall accuracy with the proposed hierarchical structure is 96% after 5 dB signal-to-noise ratio (SNR) value, approximately a 10% increase is achieved compared to the traditional classification algorithm.