A comprehensive view into MPSK modulation classification

Abstract

This paper provides a relative comprehensive overview on current modulation classification of MPSK from both the likelihood-based perspective and the feature-based perspective. Traditional methods based on maximum likelihood (ML) method mainly diverges in the way how unresolved parameters from the received signal are viewed. Some recent work adopting feature recognition such as SVM-based and Deep Learning-based classifying algorithms are also introduced. Fundamental equations are also provided for each method. This paper makes comparison among different methods in each section and explained the preferred utilization circumstance of each, aiming to help readers find the best algorithm in each of their specific case. Moreover, advantages and disadvantages of different algorithms are clearly stated for the readers information. Based on the pros and cons, it is also suggested for readers to develop new compound algorithms of better functionality for further research.