国防科技大学学报2023，Vol.45Issue(6)：78-83,6.DOI:10.11887/j.cn.202306011
面向通信信号调制识别的半监督生成对抗网络框架
Semi-supervised generative adversarial network framework for modulation recognition of communication signals
摘要
Abstract
Aiming at the problem that the accuracy of the existing modulation signal recognition model was low under the condition of weak supervision with only a small amount of labeled data,a semi supervised learning framework based on generated countermeasure network was proposed.By performing a redundant spatial transformation on the communication signals,the method can adapt to the generative adversarial network model and retain rich signal adjacent features.Through the introduction of Wasserstein generative adversarial network-gradient penalty,a semi-supervised learning framework suitable for electromagnetic signal processing was constructed to realize the effective utilization of unlabeled signal samples.In order to verify the effectiveness of the proposed algorithm,sufficient experiments were conducted on the RADIOML 2016.04C dataset.Experimental results show that the proposed method can train an efficient classifier under semi-supervised conditions and obtain excellent modulation recognition results.关键词
生成对抗网络/半监督学习框架/通信信号/调制识别Key words
generative adversarial network/semi-supervised learning framework/communication signal/modulation recognition分类
信息技术与安全科学引用本文复制引用
周华吉,徐杰,郑仕链,沈伟国,王巍,楼财义..面向通信信号调制识别的半监督生成对抗网络框架[J].国防科技大学学报,2023,45(6):78-83,6.基金项目
国家自然科学基金资助项目(61772401,U19B2015,U19B2016,61871398) （61772401,U19B2015,U19B2016,61871398）
