Version 1
: Received: 26 September 2024 / Approved: 26 September 2024 / Online: 26 September 2024 (12:48:29 CEST)
How to cite:
Di, C.; Ji, J.; Sun, C.; Liang, L. SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification. Preprints2024, 2024092099. https://doi.org/10.20944/preprints202409.2099.v1
Di, C.; Ji, J.; Sun, C.; Liang, L. SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification. Preprints 2024, 2024092099. https://doi.org/10.20944/preprints202409.2099.v1
Di, C.; Ji, J.; Sun, C.; Liang, L. SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification. Preprints2024, 2024092099. https://doi.org/10.20944/preprints202409.2099.v1
APA Style
Di, C., Ji, J., Sun, C., & Liang, L. (2024). SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification. Preprints. https://doi.org/10.20944/preprints202409.2099.v1
Chicago/Turabian Style
Di, C., Chao Sun and Linlin Liang. 2024 "SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification" Preprints. https://doi.org/10.20944/preprints202409.2099.v1
Abstract
Abstract—Traditional automatic modulation classificationmethods operate under the closed-set assumption, which provesto be impractical in real-world scenarios due to the diversenature of wireless technologies and the dynamic characteristicsof wireless propagation environments. Open-set environments introduce substantial technical challenges, particularly in terms ofdetection effectiveness and computational complexity. To addressthe limitations of modulation classification and recognition inopen-set scenarios, this paper proposes a semi-supervised openset recognition approach, termed SOAMC (Semi-SupervisedOpen-Set Automatic Modulation Classification). The primaryobjective of SOAMC is to accurately classify unknown modulation types, even when only a limited subset of samples ismanually labeled. The proposed method consists of three keystages: (1) A signal recognition pre-training model is constructedusing data augmentation and adaptive techniques to enhancerobustness. (2) Feature extraction and embedding are performedvia a specialized extraction network. (3) Label propagation isexecuted using a graph convolutional neural network (GCN) toefficiently annotate the unlabeled signal samples. Experimentalresults demonstrate that SOAMC significantly improves classification accuracy, particularly in challenging scenarios with limitedlabeled data and high signal similarity. These findings are criticalfor the practical identification of complex and diverse modulationsignals in real-world wireless communication systems.
Keywords
semi-supervised learning; automatic modulationclassification; open set recognition
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
