An Orthogonal Pseudo-Prototype and Pseudo-Target-Based Method for Few-Shot Class-Incremental Automatic Modulation Recognition
Zhiliang Deng, Chunbo Luo, Zixi Tang, Xitong Pu, Yang Luo · IEEE Transactions on Cognitive Communications and Networking · 2025
Deep learning has significantly advanced automatic modulation recognition (AMR), yet existing deep learning-based AMR methods struggle to adapt to the rapid evolution of 6G and beyond. A key challenge is their inability to incrementally integrate new modulation types without catastrophic forgetting of previously learned ones. Additionally, the annotation of wireless signals remains difficult, further limiting adaptability. To address these challenges, we introduce few-shot class-incremental learning for AMR (FSCIL-AMR) and propose the orthogonal pseudo-prototype and pseudo-target (O3PT) method. O3PT generates orthogonal pseudo-prototypes to replace ambiguous class prototypes caused by high signal feature similarity, thereby mitigating classification confusion when computing cosine similarities between features and prototypes. It also implicitly preserves feature space for future modulation types and reformulates complex prototype constraints into a more manageable alignment task. Furthermore, O3PT exploits inter-class feature similarities to generate pseudo-targets and utilizes supervised contrastive learning to impose adaptive constraints, enhancing inter-class separability while mitigating overfitting caused by limited incremental training signals. By training a lightweight projector, O3PT enables efficient network expansion as new modulation types emerge. Extensive experiments on the RML2018.01A and RML2016.10A datasets validate its effectiveness.