Maximum-likelihood modulation classification for PSK/QAM

This paper addresses automatic modulation classification for PSK and QAM signals under coherent and noncoherent conditions. In particular, the paper extends previous results by treating the classification of higher-state QAM signals. A maximum-likelihood algorithm is presented for coherent classification of PSK and QAM signals. We evaluate the algorithms performance for various PSK and QAM modulation types including 64-state QAM and then compare it with a psuedo maximum-likelihood noncoherent classification technique in terms of the error rate, false alarm rate, and computational complexity. The application of these results to the design and performance of an automatic signal recognizer is discussed throughout the paper.