This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.
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 D. I. Badreldeen & A. I. Hesham “New Algorithms for Automatic Modulation Recognition for Analogue Signals Using Multi Features” in International Journal of Signal Processing Systems vol II 2014.
 Y. T. Chan L. G. Godbois. & P. Yansouni (1985) “Identification of the modulation type of a signal” in Proc. IEEE International Conference of Acoustic Speech and Signal Processing vol 10 1985 pp. 838-841.
 W. Juan-ping H. Ying-zheng Z. Jin-mei & W. Hun-kui “Automatic Modulation Recognition of Digital Communication Signals” in 2010 First International Conference on Pervasive Computing Signal Processing and Applications 2010 pp. 590-593.
 A. K. Nandi & E. E. Azzouz “Algorithms for automatic modulation recognition of communication signals” in IEEE Transactions on Communications vol 46 1998 pg. 431-436.
 S. Ghasemi & A. Gangal “An effective algorithm for automatic modulation recognition” in Signal Processing and Communications Applications Conference (SUI) 2014 22nd pg. 903-906.