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

James A. Latshaw, D. Popescu, John A. Snoap, C. Spooner
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引用次数: 4

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.
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用胶囊网络对原始I/Q数据的数字调制信号进行分类
机器学习已经成为解决各种工程和科学领域问题的强大工具,包括通信系统领域。本文介绍了利用胶囊网络对I/Q信号分量进行数字调制信号分类的方法。通过使用两个不同的数据集,研究了经过训练的胶囊网络正确分类已训练识别的数字调制信号类别的泛化能力,这两个数据集包含相似类别的数字调制信号,但它们是独立生成的。结果表明,胶囊网络能够达到较高的分类精度。然而,这些网络容易受到数据转移问题的影响,这将在本文中讨论。
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