On Signal Phase Based Modulation Classification

We consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives derived from Gauss quadrature rules. The proposed approximate ML schemes can offer virtually optimal performance with reduced complexity. We then present a general performance analysis for classification of multiple modulation constellations.