Automatic modulation classification is very important in cognitive radio and communication reconnaissance systems. Two novel approaches for identifying the modulation format of general M-ary PSK signal are proposed, which are based on the phase entropy. Phase entropy of the first one is estimated in time domain with probability space partitioned into fixed dimensions. And for the second one, the frequency transform is first applied to the phase of the received signal and the entropy of the measured signal is then estimated. Based on a general hypothesis test, the entropies of different modulation signals are compared to classify them. The simulation results illustrate that the proposed algorithm has smaller computational complexity than existing classifier and the second one has better classification performance in low signal-to-noise ratio domain.
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