Multi-Task Learning with Dynamic Splitting for Open-Set Wireless Signal Recognition

• 作者: XU Yujie ZHAO Qingchen XU Xiaodong QIN Xiaowei CHEN Jianqiang
• 来源期刊: 中兴通讯技术（英文版）
• 年，卷（期）: 2022,（z1）
• 所属期刊栏目: Research Paper
• 页码范围: 44-55
• 页数: 12页
• 语种: 英文
• DOI: 10.12142/ZTECOM.2022S1007

摘要

Open-set recognition (OSR) is a realistic problem in wireless signal recogni?tion, which means that during the inference phase there may appear unknown classes not seen in the training phase. The method of intra-class splitting (ICS) that splits samples of known classes to imitate unknown classes has achieved great performance. However, this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment. In this paper, we train a multi-task learning (MTL) net?work based on the characteristics of wireless signals to improve the performance in new scenes. Besides, we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples. To be specific, we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold. We conduct several experi?ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset, and the analytical results demonstrate the effective?ness of the proposed method.