Ensemble of convolution neural networks for improving automatic modulation classification performance

This paper investigates convolutional neural networks (CNN) to classify 26 types of signal modulation under the influence of five different fading channels and Gausian noise with SNR from -20 dB to +18 dB. Specifically, five CNN models, including ResNet18, SqueezeNet, GoogleNet, MobileNet, and RepVGG, are taken into account for a accuracy competition to discover the best one.
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Ensemble of convolution neural networks for improving automatic modulation classification performance