A Reference Signal-Aided Deep Learning Approach for Overlapped Signals Automatic Modulation Classification
A Reference Signal-Aided Deep Learning Approach for Overlapped Signals Automatic Modulation Classification
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DOI：
10.1109/lcomm.2023.3242690
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Rui Zhang;Yanlong Zhao;Zhendong Yin;Dasen Li;Zhilu Wu
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文献类型：
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作者：
Rui Zhang;Yanlong Zhao;Zhendong Yin;Dasen Li;Zhilu Wu
传统的基于似然和手工特征的重叠信号自动调制分类（OS-AMC）方法在实际应用场景中存在重叠数的不确定性，而现有的深度学习方法仍然需要复杂的训练过程。在这封信中，提出了一种结合ConvNeXt和atrous self-attention Transformer的混合网络的深度学习方法来解决这个问题。具体而言，引入参考信号辅助训练，自动生成网络判决阈值，省去了判决阈值的搜索过程，提高了训练效率。仿真结果表明，该方法具有训练过程简单、计算复杂度低、存储开销小的特点，能够获得上级分类精度。
Traditional likelihood-based and handcrafted feature-based methods for overlapped signals automatic modulation classification (OS-AMC) suffer from the uncertainty of the overlapped numbers in practical application scenarios, while existing deep learning methods still require a complex training process. In this letter, a deep learning approach with a hybrid network combining ConvNeXt and atrous self-attention transformer is proposed to solve this problem. Specifically, a reference signal-aided training is introduced to generate the decision threshold of the proposed network automatically, which omits the searching process of the decision threshold and makes the training process more efficient. The simulation results indicate that the proposed method can achieve superior classification accuracy with a simpler training process and lower computational complexity and memory cost.