Automatic recognition of digitally modulated communications signals

This paper introduces an algorithm that extends the capability of digital modulations classifiers to cope with signals that have memory incorporated in their modulation scheme. The algorithm employs the decision-theoretic approach where the identification of different modulation types is performed by developing a set of decision criteria. The performance of the classifier has been evaluated by simulating different types of bandlimited digital signals corrupted by Gaussian noise. It is shown that the overall success rate is over 94% at the signal to noise ratio (SNR) of 10 dB with some modulation schemes detected with success rate of 100% at this SNR.