Authors: Xiaoyang Hao, Zhixi Feng, Tongqing Peng, Shuyuan Yang
Abstract: Automatic modulation classification (AMC) is an effective way to deal with
physical layer threats of the internet of things (IoT). However, there is often
label mislabeling in practice, which significantly impacts the performance and
robustness of deep neural networks (DNNs). In this paper, we propose a
meta-learning guided label noise distillation method for robust AMC.
Specifically, a teacher-student heterogeneous network (TSHN) framework is
proposed to distill and reuse label noise. Based on the idea that labels are
representations, the teacher network with trusted meta-learning divides and
conquers untrusted label samples and then guides the student network to learn
better by reassessing and correcting labels. Furthermore, we propose a
multi-view signal (MVS) method to further improve the performance of
hard-to-classify categories with few-shot trusted label samples. Extensive
experimental results show that our methods can significantly improve the
performance and robustness of signal AMC in various and complex label noise
scenarios, which is crucial for securing IoT applications.
Source: http://arxiv.org/abs/2408.05151v1
