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A Novel Multi-Feature Extraction Based Automatic Modulation Classification

Year: 2025

Type: article

Abstract: 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.

Source: Journal of Systems Engineering and Electronics

DOI: 10.23919/jsee.2025.000032

Authors: Peng Shang, Lishu Guo, Decai Zou, Xue Wang, Shuaihe Gao, Pengfei Liu

Institutions: Chinese Academy of Sciences, National Time Service Center, University of Chinese Academy of Sciences, Xidian University

Language: English

Topic: Wireless Signal Modulation Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Natural Science Foundation of China

Award: G3534650371