Modulation recognition is a fundamental task in communication systems as the
accurate identification of modulation schemes is essential for reliable signal
processing, interference mitigation for coexistent communication technologies,
and network optimization. Incorporating deep learning (DL) models into
modulation recognition has demonstrated promising results in various scenarios.
However, conventional DL models often fall short in online dynamic contexts,
particularly in class incremental scenarios where new modulation schemes are
encountered during online deployment. Retraining these models on all previously
seen modulation schemes is not only time-consuming but may also not be feasible
due to storage limitations. On the other hand, training solely on new
modulation schemes often results in catastrophic forgetting of previously
learned classes. This issue renders DL-based modulation recognition models
inapplicable in real-world scenarios because the dynamic nature of
communication systems necessitate the effective adaptability to new modulation
schemes. This paper addresses this challenge by evaluating the performance of
multiple Incremental Learning (IL) algorithms in dynamic modulation recognition
scenarios, comparing them against conventional DL-based modulation recognition.
Our results demonstrate that modulation recognition frameworks based on IL
effectively prevent catastrophic forgetting, enabling models to perform
robustly in dynamic scenarios.

该论文评估了多种增量学习算法在动态调制识别场景中的性能，并将其与传统的基于深度学习的调制识别方法进行了比较。结果表明，基于增量学习的调制识别框架能够有效防止灾难性遗忘，使模型能够在动态场景中稳健地执行。