Blind Digital Modulation Identification Using an Efficient Method-of-Moments Estimator

The automatic identification of the modulation format of a detected signal is a major task of an intelligent receiver in both military and civilian applications. It is well known that the maximum likelihood (ML) classifier requires a priori knowledge of the incoming signal and channel (including amplitude, timing information, noise power, and the roll-off factor of the pulse-shaping filter). To relax this requirement, we introduce a novel estimator to estimate the parameters required by the ML classifier which is blind to the modulation scheme of the received signal, and this gives rise to a new blind modulation classifier for digital amplitude-phase modulated signals. While the proposed classifier is completely blind, the simulation results show that the performance of this classifier is very close to the optimal non-blind classifier.