A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification
文献类型：期刊论文
| 作者 | Zhou, Xiaoyu2; Qi, Peihan2; Liu, Qi1 ; Ding, Yuanlei2; Zheng, Shilian3; Li, Zan2
|
| 刊名 | CHINA COMMUNICATIONS
 |
| 出版日期 | 2024-03-31 |
| 页码 | 16 |
| 关键词 | deep learning few-shot label propaga- tion modulation classification semi-supervised learn- ing |
| ISSN号 | 1673-5447 |
| DOI | 10.23919/JCC.ea.2022-0339.202401 |
| 通讯作者 | Qi, Peihan(phqi@xidian.edu.cn) |
| 英文摘要 | With the successive application of deep learning (DL) in classification tasks, the DL -based modulation classification method has become the preference for its state-of-the-art performance. Nevertheless, once the DL recognition model is pre -trained with fixed classes, the pre -trained model tends to predict incorrect results when identifying incremental classes. Moreover, the incremental classes are usually emergent without label information or only a few labeled samples of incremental classes can be obtained. In this context, we propose a graphbased semi -supervised approach to address the fewshot classes -incremental (FSCI) modulation classification problem. Our proposed method is a twostage learning method, specifically, a warm-up model is trained for classifying old classes and incremental classes, where the unlabeled samples of incremental classes are uniformly labeled with the same label to alleviate the damage of the class imbalance problem. Then the warm-up model is regarded as a feature extractor for constructing a similar graph to connect labeled samples and unlabeled samples, and the label propagation algorithm is adopted to propagate the label information from labeled nodes to unlabeled nodes in the graph to achieve the purpose of incremental classes recognition. Simulation results prove that the proposed method is superior to other finetuning methods and retrain methods. |
| 资助项目 | National Natural Science Foundation of China[62171334] ; National Natural Science Foundation of China[11973077] ; National Natural Science Foundation of China[12003061] |
| WOS研究方向 | Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001196701600001 |
| 出版者 | CHINA INST COMMUNICATIONS |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.xao.ac.cn/handle/45760611-7/6226]  |
| 专题 | 研究单元未命名 |
| 通讯作者 | Qi, Peihan |
| 作者单位 | 1.Chinese Acad Sci, Xinjiang Astron Observ, Urumqi 830011, Peoples R China 2.Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 3.011 Res Ctr, Sci & Technol Commun Informat Secur Control Lab, Jiaxing 314033, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Xiaoyu,Qi, Peihan,Liu, Qi,et al. A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification[J]. CHINA COMMUNICATIONS,2024:16. |
| APA | Zhou, Xiaoyu,Qi, Peihan,Liu, Qi,Ding, Yuanlei,Zheng, Shilian,&Li, Zan.(2024).A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification.CHINA COMMUNICATIONS,16. |
| MLA | Zhou, Xiaoyu,et al."A Graph-Based Semi-Supervised Approach for Few-Shot Class-Incremental Modulation Classification".CHINA COMMUNICATIONS (2024):16. |
入库方式： OAI收割
来源：新疆天文台
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