SigDA: A Superimposed Domain Adaptation Framework for Automatic Modulation Classification

作     者：Wang, Shuang Xing, Hantong Wang, Chenxu Zhou, Huaji Hou, Biao Jiao, Licheng 

作者机构：Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Natl Key Lab Electromagnet Space Secur Jiaxing 314033 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS》 (IEEE Trans. Wireless Commun.)

年 卷 期：2024年第23卷第10期

页      面：13159-13172页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 

基　　金：National Key Research and Development Program of China [2021ZD0110400, 2021ZD0110404] National Natural Science Foundation of China [62271377, U22B2054] Key Research and Development Program of Shannxi Program [2021ZDLGY0106, 2022ZDLGY0112] Key Scientific Technological Innovation Research Project by the Ministry of Education 

主　　题：Feature extraction Task analysis Modulation Adaptation models Convolution Data models Wireless communication Automatic modulation classification domain adaptation multi-task learning adversarial training 

摘      要：Due to the uncertainty of non-cooperative communication channels, the received signals often contain various impairment factors, leading to a significant decline in the performance of existing deep learning (DL)-based automatic modulation classification (AMC) models. Several preliminary works utilize domain adaptation (DA) to alleviate this issue, however, they are constrained by singular domain difference factor, whereas in practice, these factors often manifest cumulatively. Therefore, this paper introduce a more realistic task named superimposed DA, where multiple domain difference factors are overlaid, reflecting the cumulative nature of them. We propose the SigDA as a solution framework, which adopts adversarial training to align the data distribution in different domains. Two technical modules, Multi-task based Masked Signal Feature Extractor (M2SFE) and Signal Feature Pyramid Aggregation (SFPA), are innovatively designed in SigDA. M2SFE utilizes mask and reconstruction task to enhance feature extraction and achieves discriminative feature selection through the design of feature mapping layers, while SFPA can solve the problem of inconsistent signal length in superimposed DA and can aggregate the features of signals into the same dimension. We consider and superimpose various typical signal domain difference factors, comprehensive experiments demonstrate that the proposed framework can achieve significant performance improvement in various communication channels.