Blind identification of QAM signals using a likelihood-based algorithm

This paper presents a method for automatically identifying different QAM modulations. This method identifies the modulation type as the hypothesis for which the likelihood function of the amplitudes of the received signal is the maximum. The derivation of the likelihood functions assumes additive white Gaussian noise and no pulse shaping. In order to accommodate pulse shaping in the received signal, the system sub-samples the incoming signals non-uniformly so that the distribution of the amplitudes of the sub-sampled signals approximately matches that of QAM signals without pulse shaping. This method does not need prior knowledge of carrier frequency and baud rate and can identify modulation types at relatively low SNRs and with relatively few symbols. Simulation results demonstrating accurate modulation identification in the presence of additive noise are included in the paper. Results presented in the paper with non-Gaussian noise indicate that the system is robust to variations from the assumed noise model.