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Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification | IEEE Journals & Magazine | IEEE Xplore

Complex-Valued Depthwise Separable Convolutional Neural Network for Automatic Modulation Classification


Abstract:

Automatic modulation classification (AMC) is a critical task in industrial cognitive communication systems. Existing state-of-the-art methods, typified by real-valued con...Show More

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 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.
Article Sequence Number: 2522310
Date of Publication: 01 August 2023

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