Research on the Classification and Identification of Modulation Methods for W-Band Millimeter Wave Communication

This paper presents a classification method based on feature extraction and neural networks for the identification of higher-order modulation techniques in the W-band under additive white Gaussian noise (AWGN) conditions. An expression for the tenth-order cumulants has been derived. Simulation results demonstrate that when the signal-to-noise ratio (SNR) is no less than 8dB, the classification accuracy can be maintained above 95%, thus validating the effectiveness of this method.