Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification
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 componen...
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Veröffentlicht in: | IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-10 |
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Sprache: | eng |
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Zusammenfassung: | 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 fine-tuning 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. |
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ISSN: | 0018-9456 1557-9662 |
DOI: | 10.1109/TIM.2023.3298657 |