Accuracy Analysis of Feature-based Automatic Modulation Classification with Blind Modulation Detection

The process of automatic classification of a detected signal's employed modulation type has gained importance in recent years. The goal of such an approach is to maximize the achievable throughput for intelligent receiver designs in civilian applications as well as jamming malicious signals in military applications. Automatic Modulation Classification (AMC) increases in difficulty since there is no a-priori knowledge of transmitted signal properties, such as signal power, carrier frequency, or bandwidth, nor any associated link properties such as channel state information (CSI), noise characteristics, signal-to-noise ratio (SNR) or any offset in frequency and phase. The most complex, albeit also most realistic, scenarios for AMC are faced when considering Non-Gaussian noise with multipath fading in frequency selective and time-varying channels. Different methods have been proposed in the literature to estimate unknown signals and channel parameters for AMC. However, a key consideration in selecting among them is attaining low computational complexity in order for AMC to become a technique feasible for real-time applications. Predominantly, blind AMC and associated parameter estimation utilizes feature-based approaches, owing to their low-complexity calculations of statistical values. In this work, we have analyzed the accuracy of High-order Statistics-based (HoS) methods utilizing feature extraction approaches, Support Vector Machine classifiers, and estimation techniques to determine an optimized framework for different real-time applications.