A recent trend of automatic modulation classification is to automatically learn high-level abstraction of signals, instead of manually designing features for further classification. In this paper, we propose a new deep geometric convolutional network (DGCN) to hierarchically extract discriminative features from Wigner–Ville distribution map of signals. A group of geometric filters are constructed from a least square support vector machine, to capture the linear singularity existed in maps. The filters are cascaded to construct a deep network for extracting discriminative features and classifying signals with different modulation types. Some experiments are taken to investigate the performance of DGCN, and the results show that it can achieve high accuracy in classifying 15 types of modulation signals.

中文翻译：

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用于自动调制分类的深度几何卷积网络

自动调制分类的最新趋势是自动学习信号的高级抽象，而不是手动设计特征以进行进一步分类。在本文中，我们提出了一种新的深度几何卷积网络 (DGCN)，以从信号的 Wigner-Ville 分布图中分层提取判别特征。一组几何滤波器由最小二乘支持向量机构建，以捕捉地图中存在的线性奇异点。滤波器级联以构建深度网络，用于提取判别特征并对具有不同调制类型的信号进行分类。对DGCN的性能进行了一些实验研究，结果表明它在对15种调制信号进行分类时可以达到较高的精度。