Blind Phase-Amplitude Modulation Classification with Unknown Phase Offset

Abstract

This paper first discusses the maximum likelihood (ML) classifier for automatic classification of digital modulations. The classifier is optimum for classification of phase-amplitude modulated signals under ideal environment. However, this is not the case in the presence of phase offset owing to inaccurate estimation. In this paper, we propose a novel non-coherent ML classifier to mitigate the effect phase offset. The non-coherent ML classifier adopts a pre-classification phase correction stage through a closed form estimator based on higher order statistics. Experimental results show improvement of classification accuracy at reasonable signal to noise ratio