论文标题
使用胶囊网络和循环累积特征对数字调制信号进行了强大的分类
Robust Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulant Features
论文作者
论文摘要
本文研究了使用胶囊网络和环状累积（CC）特征对数字调制信号进行鲁棒分类的问题，该特征是通过环固化信号处理（CSP）提取的。研究中使用了两个不同的数据集，这些数据集包含类似类似的数字调制信号但已独立生成的数据集，该数据集在研究中使用，这表明使用CCS训练的胶囊网络达到了高分类的精度，同时在分类准确性方面也优于其他基于深度学习的方法以及总体化能力。
The paper studies the problem of robust classification of digitally modulated signals using capsule networks and cyclic cumulant (CC) features extracted by cyclostationary signal processing (CSP). Two distinct datasets that contain similar classes of digitally modulated signals but that have been generated independently are used in the study, which reveals that capsule networks trained using CCs achieve high classification accuracy while also outperforming other deep learning-based approaches in terms of classification accuracy as well as generalization abilities.
