PASS-Net: A Pseudo Classes and Stochastic Classifiers-Based Network for Few-Shot Class-Incremental Automatic Modulation Classification

Recently, significant progress has been made in deep learning, which has been widely applied in automatic modulation classification (AMC) with remarkable outcomes. However, current deep learning based AMC (DL-AMC) algorithms show limitations in their ability to accommodate dynamically changing communication scenarios. With the increasing number of modulation types, most DL-AMC algorithms often need to be re-trained, making it hard to transfer previous knowledge to the new models. Also, modulation classification faces the difficulty of acquiring and annotating a large number of signals. To address these challenges, we have modeled a few-shot class-incremental AMC (FSCI-AMC) task and proposed a pseudo classes and stochastic classifiers-based network (PASS-Net) to accomplish it. Firstly, the pseudo classes are generated to reserve space for new types, enhancing the model’s continuous learning capability. Additionally, stochastic classifiers ensure the reliability of generated pseudo classes. Finally, in the incremental session, both real and pseudo classes are used for modulation classification. To evaluate the proposed approach, experiments were conducted on 7 modulation types as base classes and another 7 modulation types as incremental classes. The results show superior performance in 7 sessions of 1-way 1-shot and 1-way 5-shot class-incremental experiments compared to other competitive methods.