Multi-Task Learning with Dynamic Splitting for Open-Set Wireless Signal Recognition
XU Yujie1, ZHAO Qingchen1, XU Xiaodong1, QIN Xiaowei1, CHEN Jianqiang2
(1. University of Science and Technology of China, Hefei 230026, China;
2. ZTE Corporation, Shenzhen 518057, China)
Open-set recognition (OSR) is a realistic problem in wireless signal recognition, 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) which 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 with huge performance degradation in new environment. In this paper, we train a multi-task learning (MTL) network 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 perturb 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 experiments 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 effectiveness of the proposed method.
open-set recognition; dynamic method; adversarial direction; multi-task learning; wireless signal