Deep-learning-based digital signal modulation mode identification

Abstract

In modern electronic warfare, in order to correctly identify the intercepted enemy signals, it is first necessary to identify the modulation system used by the enemy signals and correctly demodulate them in order to obtain the information transmitted by the enemy and seize the opportunity in informationized warfare. Aiming at the low recognition rate of digital signal modulation recognition under low signal-to-noise ratio, the recognition model suitable for signal constellation diagram is improved on the basis of the classical convolutional neural network GoogLeNet network, and a recognition model suitable for signal constellation diagram is constructed. Experimental results show that when the signal-to-noise ratio SNR=5dB, the average classification accuracy of the 12 hybrid modulation types can reach 95.28%, and the classification accuracy of the improved GoogLeNet is increased by 1.1%.