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A novel multi-feature extraction based automatic modulation classification
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AI
DOI：10.23919/JSEE.2025.000032.png)
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Abstract
En 中文
Automatic modulation classification(AMC) is an essential technique in both civil and military applications. While deep learning has surpassed traditional methods in accuracy, distinguishing high-order modulations remain challenging. Current efforts prioritize complex network designs, neglecting the integration of deep features and tailored feature engineering to reslove high-order ambiguities. Therefore, a multi-feature extraction framework is proposed, which directly concatenates the deep feature extracted by a newly designed lightweight neural network and the proposed spectrum secondary features or de-noised high-order statistical features. The proposed features and lightweight network both demonstrate superior overall accuracy than other competing features or networks. Furthermore, the effectiveness of the feature extraction framework is also validated. The average classification accuracy on high-order modulation sets reaches 67.39% on the RadioML2018.01A dataset, increasing more than 2% compared with the other competitive networks under the framework. The results indicate the effectiveness of the proposed feature extraction framework for its representational ability by combing the deep features with the proposed domain features.
Keywords:
automatic modulation classification (AMC)
spectrum secondary features
de-noised high-order statistics
lightweight attention network
Journal
J
IF:
2.1
Papers:
84
Citations:
2.6K