Machine learning has become a powerful tool for solving problems in various
engineering and science areas, including the area of communication systems.
This paper presents the use of capsule networks for classification of digitally
modulated signals using the I/Q signal components. The generalization ability
of a trained capsule network to correctly classify the classes of digitally
modulated signals that it has been trained to recognize is also studied by
using two different datasets that contain similar classes of digitally
modulated signals but that have been generated independently. Results indicate
that the capsule networks are able to achieve high classification accuracy.
However, these networks are susceptible to the datashift problem which will be
discussed in this paper.Comment: 6 pages, 9 figures, to be published in IEEE ICC 2022: IEEE
International Conference on Communications 202