PID: A Parameter-Efficient Isolation Domain-Incremental Learning Framework for Signal Modulation Classification

Deep neural networks have achieved promising progress in signal modulation classification (SMC), playing an essential role in a variety of applications such as cognitive radio networks, cyber defense, and electronic surveillance. However, most existing SMC methods still follow the traditional machine learning paradigm that trains on static closed datasets, lacking the ability to cope with the challenge of continuous data distribution shifts in real communication scenarios. Directly applying the model to a new environment may lead to severe degradation of classification performance on previous scenarios, i.e., catastrophic forgetting. To address this, this article proposes the first domain-incremental learning (DIL) paradigm for SMC and designs a parameter-efficient isolation DIL (PID) method, which enables SMC models to rapidly adjust to new scenarios by extending only a few parameters, while significantly retaining classification capabilities on previous scenarios. Specifically, we first propose a parameter space decomposition-based classifier (PSD), separating the model parameters into a set of bases and corresponding coefficients. By freezing the bases and fine-tuning the low-dimensional coefficients, the catastrophic forgetting problem can be efficiently eliminated. Furthermore, we design a scene-aware domain controller (SDC) to select the most suitable domain-specific coefficients for each sample, thereby maintaining the SMC model's classification capabilities across all domains. The extensive experimental results show the superiority of the proposed PID, which achieves state-of-the-art (SOTA) overall performance. The code will be available at: https://github.com/SMC-IL/PID.