Abstract
A modulation pattern recognition method for digital modulation signals, 4ASK, BPSK, QPSK, 2FSK and 4FSK digital modulation signals, which is based on deep learning model of deep belief network is proposed. The modulation signal is pre-processed and its high order cumulants are calculated as input training features. Solutions to the problem that the same high Modulation signals are generated in different SNR environments. Using the semi-supervised learning characteristics of deep confidence network, data sets are obtained to train the parameters of deep Confidence network layer by layer for feature extraction and recognition of modulation modes. The simulation results show that the recognition rate of this method is ideal.
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