  The paper considers the problem of deep-learning-based classification of
digitally modulated signals using I/Q data and studies the generalization
ability of a trained neural network (NN) to correctly classify digitally
modulated signals it has been trained to recognize when the training and
testing datasets are distinct. Specifically, we consider both a residual
network (RN) and a convolutional neural network (CNN) and use them in
conjunction with two different datasets that contain similar classes of
digitally modulated signals but that have been generated independently using
different means, with one dataset used for training and the other one for
testing.
