A Method for Modulation Recognition Based on Entropy Features and Random Forest

Since the low SNR environment, generally the modulation recognition rate of signal modulation type is not very high. In this paper, we studied an automatic recognition method of communication signal modulation type in the low SNR. According to analyze the signal entropy as the feature, three characteristics are selected, and the random forest is as the classifier, finally we get a high recognition-rate of several communication signal modulation types in low SNR. Via simulation, it is demonstrated that the method achieves excellent performance to recognize signal type under different signal-to-noise ratio (SNR). The signal recognition rate is more than 95% when SNR is higher than 5 dB except QPSK signal. In a word, it is simple to design the recognition system, and it will have important application value.