Enhanced Automatic Modulation Classification using Deep Convolutional Latent Space Pooling
Clayton A. Harper, Lauren Lyons, Mitchell Aaron Thornton, Eric C. Larson · 2020
In our work, we investigate alternative forms of automatic modulation classification with deep learning and statistical methods. With a growing number of devices communicating through wireless transmission mediums, automatic modulation classification plays a critical role in reading an observed signal. We compare our proposed method with the current state of the art and show that traditional convolutional neural networks can outperform residual neural networks for the task of modulation classification. Using an approach inspired by research from the speaker verification community, we show that the modulation method used to transmit a signal can be classified into one of 24 candidate modulation types with greater than 98% accuracy for signals with high signal to noise ratios.