可推广且可解释的基于形状let的大型语言模型在射频指纹中的应用
Generalizable and Interpretable RF Fingerprinting with Shapelet-Enhanced Large Language Models
AI总结：
本文提出了一种结合形状let和预训练LLM的框架，用于实现高效、可解释且可推广的射频指纹识别，通过捕捉局部时间模式和全局上下文信息，提升跨领域性能。
摘要：
深度神经网络（DNNs）在无线设备认证中的射频（RF）指纹识别中取得了显著的成功。然而，其在实际部署中面临两个主要限制：领域偏移，即在一种环境中训练的模型在其他环境中难以泛化，以及DNN的黑盒性质，这限制了可解释性。为了解决这些问题，我们提出了一种新的框架，将一组可变长度的二维（2D）形状let与预训练的大型语言模型（LLM）相结合，以实现高效、可解释且可推广的RF指纹识别。2D形状let明确捕捉了I/Q组件中的多样化局部时间模式，提供了紧凑且可解释的表示。同时，预训练的LLM捕捉了更长的依赖关系和全局上下文信息，使模型能够通过最小的训练开销实现强大的泛化能力。此外，我们的框架还支持少量样本推理的原型生成，增强了跨领域性能，而无需额外的重新训练。为了评估我们所提方法的有效性，我们在六个不同协议和领域的数据集上进行了广泛的实验。结果表明，我们的方法在源域和未见过的域中都实现了优越的标准和少量样本性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
