In this work, the Transformer Network (TRN) is applied to the automatic modulation classification (AMC) problem for the first time. Different from the other deep networks, the TRN can incorporate the global information of each sample sequence and exploit the information that is semantically relevant for classification. In order to illustrate the performance of the proposed model, it is compared with four other deep models and two traditional methods. Simulation results show that the proposed one has a higher classification accuracy especially at low signal to noise ratios (SNRs), and the number of training parameters of the proposed model is less than those of the other deep models, which makes it more suitable for practical applications.

中文翻译：

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基于变压器网络的信号调制分类


在这项工作中，变压器网络（TRN）首次应用于自动调制分类（AMC）问题。与其他深度网络不同，TRN 可以合并每个样本序列的全局信息，并利用与分类语义相关的信息。为了说明所提出模型的性能，将其与其他四个深度模型和两种传统方法进行了比较。仿真结果表明，该模型具有较高的分类精度，特别是在低信噪比（SNR）下，并且该模型的训练参数数量少于其他深度模型，这使得该模型更适合实际应用应用程序。