C-SRCIL: Complex-valued Class-Incremental Learning for Signal Recognition

In military and civilian scenarios, many signal recognition tasks are often accompanied by the growth of signal classes due to signal camouflage or increased communication devices. However, conventional approaches based on deep learning cannot effectively cope with the growth of classes. To address this problem, in this paper, we propose a complex-valued class-incremental learning (CIL) framework for signal recognition (C-SRCIL), which can extract the latent complex-valued features of modulation signals and complete the CIL for complex-valued signal recognition. In our C-SRCIL, We first decouple the feature representation from the classifier to combat catastrophic forgetting, and introduce complex-valued neural networks and an integrated loss function in the feature representation. Then a complex-valued CIL adapter is designed based on the nodalization idea for updating the decoupled classifier in the incremental stage. Experiments on datasets RADIOML2016.10A and SIGNAL2022 show that C-SRCIL brings about a 1.32% performance improvement with a 39.96% reduction in training time compared to the state-of-the-art. It suggests that C-SRCIL extends the classification boundaries of existing models while preserving previous knowledge.