Incremental learning of radio modulation classification based on sample recall

Radio modulation classification has always been an important technology in the field of communications. The difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old tasks. In this paper, we propose a sample memory and recall framework for incremental learning of radio modulation classification. For data with different signal-to-noise ratios, we use a partial memory strategy by selecting appropriate samples for memorizing. We compare the performance of our proposed method with three baselines through a large number of simulation experiments. Results show that our method achieves far higher classification accuracy than fine-tuning method and feature extraction method. Furthermore, it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.