CNN-Based Automatic Modulation Recognition of Wireless Signal

Abstract

Based on simulation samples of open source data collection and communication system signal pretreatment methods, we design a Convolution Neural Network (CNN) that contains three full connection layers and three convolution layers to recognize 11 different kinds of wireless signal modulation. In additive white Gaussian noise(AWGN) model, CNN networks can realize automatic modulation classification of the wireless signal by learning the deep characters of sample data, and the average recognition accuracy is up to 81% under different ratio of signal to noise (SNR). We analyze the influence between modulation recognition accuracy and network hyperparameters such as network structures, convolutional layers, dropout value, batch size, and network training methods.