一句话：提出基于电磁信号特征蜕变关系的数据增强工具 ElecDaug，为 AMC 模型修复生成增强数据。
深度学习的自动调制分类（AMC）模型在电磁信号检测中受到广泛关注，但其鲁棒性依赖大量高质量电磁信号数据。人工采集成本高，自动生成数据质量低，导致 AMC 模型存在缺陷，因此需要通过数据增强生成电磁数据。本文提出新型电磁数据增强工具 ElecDaug，利用电磁信号特征指导蜕变过程，实现自动数据增强。该方法基于电磁数据预处理，并在传输域或时频域进行特征蜕变，从而扩充数据样本，用于构建鲁棒的 AMC 模型。初步实验表明，ElecDaug 能有效增强现有数据样本并用于模型修复。论文附带演示视频和开源代码链接。
With the application of deep learning (DL) in signal detection, improving the robustness of classification models has received much attention, especially in automatic modulation classification (AMC) of electromagnetic signals. A large amount of electromagnetic signal data is required to obtain robust models in the training and testing process. However, the high cost of manual collection and the issue of low quality of automatically generated data contribute to the AMC model’s defects. Therefore, it is essential to generate electromagnetic data by data augmentation. In this paper, we propose a novel electromagnetic data augmentation tool, namely ElecDaug, which directs the metamorphic process by electromagnetic signal characteristics to achieve automatic data augmentation. Based on electromagnetic data pre-processing, transmission or time-frequency domains characteristic metamorphic, ElecDaug can augment the data samples to build robust AMC models. Preliminary experiments show that ElecDaug can effectively augment available data samples for model repair. The video is at https://youtu.be/x5g6IVX_Q3s. Documentation and source code can be found here: https://github.com/ehhhhjw/tool_ElecDaug.git.
@inproceedings{paperbot7171,
title = {ElecDaug: Electromagnetic Data Augmentation for Model Repair based on Metamorphic Relation},
author = {Jiawei He and Zhida Bao and Quanjun Zhang and Weisong Sun and Jiawei Liu and Chunrong Fang and Yun Lin},
booktitle = {ASE},
year = {2022},
doi = {10.1145/3551349.3559536}
}