基于Transformer的通信信号调制识别方法

提出一种基于Transformer模型的通信信号调制识别方法：在数据准备阶段，构建一个不同符号速率调制识别(DSRMR)数据集；在数据预处理阶段，提出I/Q数据增强方法，用于满足模型训练在数量上和多样性的要求，增强了模型泛化能力；在模型构建阶段，将切片序列化的方法引入调制识别Transformer模型中，用于优化Transformer神经网络模型的输入问题。实验结果证明，基于Transformer模型的通信信号调制识别方法能够获得较高的信号自动调制识别准确率。

A modulation recognition method of communication signal based on Transformer

A communication signal modulation recognition method based on Transformer model is proposed. In the data preparation stage, a Different Symbol Rate Modulation Recognition(DSRMR) data set is constructed. In the data preprocessing stage, a method of I/Q data enhancement is proposed to meet the quantitative and diverse requirements of model training, and to enhance the generalization ability of the model. In the model construction stage, the method of slice serialization is introduced into the modulation recognition Transformer model, and it is employed to optimize the input problem of the Transformer neural network model. Experimental results prove that the communication signal modulation recognition method based on the Transformer model can obtain high-precision in signal automatic modulation recognition.