Code for "Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification". [Paper]

Chenghong Xiao, Shuyuan Yang, Senior Member, IEEE and ZhixiFeng, Member, IEEE

Abstract—Automatic modulation classification (AMC) is a critical task in industrial cognitive communication systems. Existing state-of-the-art methods, typified by real-valued convolutional neural networks, have introduced innovative solutions for AMC. However, such models viewed the two constituent components of complex-valued modulated signals as discrete real-valued inputs, causing structural phase damage to original signals and reduced interpretability of the model. In this article, a novel end-to-end AMC model called a complex-valued depthwise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation units to enable automatic complex-valued feature learning specifically tailored for AMC. Considering the limited hardware resources available in industrial scenarios, complex-valued depthwise separable convolution (CDSC) is designed to strike a balance between classification accuracy and model complexity. With an overall accuracy (OA) of 62.63% on the RadioML2016.10a dataset, CDSCNN outperforms its counterparts by 1%–11%. After finetuning on the RadioML2016.10b dataset, the OA reaches 63.15%, demonstrating the robust recognition and generalization capability of CDSCNN. Moreover, the CDSCNN exhibits lower model complexity compared to other methods.

We conducted experiments on two datasets, namely RadioML2016.10a, and RadioML2016.10b.

• python == 3.10.4
• pytorch == 1.12.0
• scikit-learn == 1.3.0
• numpy == 1.21.5

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@ARTICLE{10198896,
	author={Xiao, Chenghong and Yang, Shuyuan and Feng, Zhixi},
	journal={IEEE Transactions on Instrumentation and Measurement}, 
	title={Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification}, 
	year={2023},
	volume={72},
	pages={1-10},
	doi={10.1109/TIM.2023.3298657}
}