Multi-Task Based Deep Learning Approach for Open-Set Wireless Signal Identification in ISM Band

Abstract

Wireless signal identification plays an important role in effectively implementing spectrum monitoring and management. However, in ISM (Industrial, Science and Medical) band, it becomes a challenging task due to the heterogeneity of variously emerging wireless techniques, and part of the potential unknown spectral occupants may even hinder the feasibility of wireless signal identification. To overcome such difficulties, we focus on open-set recognition (OSR) in this paper and present a multi-task learning architecture based on deep neural network for identifying known and unknown spectral occupants. A novel structured extension of the counterfactual GAN (CountGAN) architecture is proposed and we introduce a multi-tasking architecture to take advantage of the modulation-domain information from the captured signal, thus improving the representation of individual wireless signals and further enhancing model robustness and adaptability to open-set scenarios. In particular, Circle-loss in metric learning and extreme value theory are also applied to make tighter and clearer decision boundaries for signal identification, and further enhance the optimization of the decision boundaries between known and unknown classes. Numerical results indicate that the proposed framework consistently outperforms state-of-the-art OSR algorithms and several baselines for wireless signal identification task, both in terms of convergence performance and classification accuracy.