论文标题
使用胶囊网络将数字调制信号与RAW I/Q数据分类
Using Capsule Networks to Classify Digitally Modulated Signals with Raw I/Q Data
论文作者
论文摘要
机器学习已成为解决各种工程和科学领域（包括通信系统领域）中问题的强大工具。本文介绍了使用胶囊网络用于使用I/Q信号组件分类数字调制信号。还通过使用两个包含类似类似的数字调制信号类似类似类别的类别类别但已独立生成的不同数据集，研究了经过训练的数字调制信号的类别，但已独立生成，也研究了经过训练的数字调制信号类别的概括能力。结果表明胶囊网络能够达到高分类精度。但是，这些网络容易受到Datashift问题的影响，该问题将在本文中进行讨论。
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.
