Digital Modulation Recognition Method Based on High-Order Cumulant Feature Learning

Automatic modulation recognition (AMR) of communication signals is an important research topic in the processing of intercepted signals. In this paper, aiming for the automatic recognition of modulated signals, we propose a feature learning and classification method based on the high-order cumulants, which effectively suppresses Gaussian white noise. Six digital modulation schemes including BPSK, QPSK, 8PSK, 8QAM, 16QAM, and 64QAM can be recognized by comparing the feature with the threshold. During the experiments, we plot the confusion matrix under different conditions. Moreover, it is derived and verified with simulation experiments and actual data acquisition.