Deep neural networks (DNNs) have achieved remarkable success in radio frequency (RF) fingerprinting for wireless device authentication. However, their practical deployment faces two major limitations: domain shift, where models trained in one environment struggle to generalize to others, and the black-box nature of DNNs, which limits interpretability. To address these issues, we propose a novel framework that integrates a group of variable-length two-dimensional (2D) shapelets with a pre-trained large language model (LLM) to achieve efficient, interpretable, and generalizable RF fingerprinting. The 2D shapelets explicitly capture diverse local temporal patterns across the in-phase and quadrature (I/Q) components, providing compact and interpretable representations. Complementarily, the pre-trained LLM captures more long-range dependencies and global contextual information, enabling strong generalization with minimal training overhead. Moreover, our framework also supports prototype generation for few-shot inference, enhancing cross-domain performance without additional retraining. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on six datasets across various protocols and domains. The results show that our method achieves superior standard and few-shot performance across both source and unseen domains.

翻译：深度神经网络在无线设备认证的射频指纹识别领域取得了显著成功。然而，其实际部署面临两大主要限制：一是域偏移问题，即在某一环境下训练的模型难以泛化至其他环境；二是深度神经网络的黑箱特性，限制了模型的可解释性。为解决这些问题，我们提出了一种新颖框架，该框架将一组可变长度的二维形状与预训练大语言模型相结合，以实现高效、可解释且可泛化的射频指纹识别。二维形状显式地捕获了同相与正交分量间多样化的局部时序模式，提供了紧凑且可解释的特征表示。与之互补，预训练大语言模型能够捕捉更长程的依赖关系和全局上下文信息，从而以极小的训练开销实现强大的泛化能力。此外，本框架还支持原型生成以进行少样本推理，无需额外重新训练即可提升跨域性能。为评估所提方法的有效性，我们在涵盖多种协议与领域的六个数据集上进行了广泛实验。结果表明，该方法在源域及未见域上均实现了优异的基准性能和少样本性能。