Modulation Recognition in Continuous Phase Modulation Using Approximate Entropy

Abstract

Modulation recognition finds its application in today's cognitive systems ranging from civilian to military installations. Existing modulation classification algorithms include classic likelihood approaches and feature-based approaches. In this study, approximate entropy, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (CPM) signal. The method is then extended to classify CPM signals with differing pulse shapes, which include raised cosine and Gaussian pulses with varying roll-off factors and bandwidth-time products, respectively. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.