Phasma: An automatic modulation classification system based on Random Forest

Abstract

We propose an architecture that incorporates an automatic modulation classification (AMC) mechanism, assisted by Random Forest machine learning (ML) classifiers. Using this architecture we are able to distinguish a variety of digital and analog modulation schemes under various SNR environments.