  The paper studies the problem of robust classification of digitally modulated
signals using capsule networks and cyclic cumulant (CC) features extracted by
cyclostationary signal processing (CSP). Two distinct datasets that contain
similar classes of digitally modulated signals but that have been generated
independently are used in the study, which reveals that capsule networks
trained using CCs achieve high classification accuracy while also outperforming
other deep learning-based approaches in terms of classification accuracy as
well as generalization abilities.
