Automatic Modulation Classification Based on CNN-Transformer Graph Neural Network
Dong Wang,
Meiyan Lin,
Xiaoxu Zhang
et al.
Abstract:In recent years, neural network algorithms have demonstrated tremendous potential for modulation classification. Deep learning methods typically take raw signals or convert signals into time–frequency images as inputs to convolutional neural networks (CNNs) or recurrent neural networks (RNNs). However, with the advancement of graph neural networks (GNNs), a new approach has been introduced involving transforming time series data into graph structures. In this study, we propose a CNN-transformer graph neural ne… Show more
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