Performance Evaluation of Feature-based Automatic Modulation Classification

Automatic Modulation Classification (AMC) is a vital component for intelligent receivers. Due to the rapid growth of wireless communications, modulation classification has become a critical factor for efficient and high-performing receiver designs. Automatic modulation classification impacts critical civilian and military applications. In civilian applications, various forms of quaternary and 16-point modulations (QASK, QFSK, QPSK, 16-QAM, etc.) are deployed. For this group of modulations, the two dominant AMC approaches proposed in the literature are Likelihood- (LB) and Feature-based (FB) classification. Feature-based approaches focus on five main properties: Signal Spectral-based Features, Wavelet Transform-based Features, High-order Statistics-based (HoS) Features, Cyclostationary Analysis-based Features and Graph-based Cyclic-Spectrum Analysis Features, all of which are employed to extract initial information for the classifier. In this work, we analyze the performance of automatic modulation classification applied for civilian modulations with various features under practical scenarios.