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Maximum Likelihood Based Modulation Classification for Unsynchronized QAMs

We consider modulation classification for quadrature amplitude modulation (QAM) formats. The received signal is assumed to be unsynchronized in both time and frequency, since in practice the receiver has little prior knowledge about the transmitted signal. To tackle this challenging problem, we propose a classifier that is based on a combination of blind time synchronization, differential processing, and maximum likelihood (ML) detection. A computationally efficient scheme is then developed. Numerical results are provided to justify our approach.