Using Capsule Networks to Classify Digitally Modulated Signals with Raw I/Q Data

TL;DR

This paper explores the application of capsule networks for classifying digitally modulated signals directly from raw I/Q data, demonstrating high accuracy but also highlighting challenges like data shift.

Contribution

It introduces the use of capsule networks for raw I/Q signal classification and studies their generalization ability across different datasets.

Findings

Capsule networks achieve high classification accuracy.

They are susceptible to data shift problems.

Generalization across datasets is promising but limited by data shift.

Abstract

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.