Radio frequency fingerprints (RFFs) enable secure wireless authentication but struggle in open-set scenarios with unknown devices and varying channels. Existing methods face challenges in generalization and incur high computational costs. We propose a lightweight, self-adaptive RFF extraction framework using Low-Rank Adaptation (LoRA). By pretraining LoRA modules per environment, our method enables fast adaptation to unseen channel conditions without full retraining. During inference, a weighted combination of LoRAs dynamically enhances feature extraction. Experimental results demonstrate a 15% reduction in equal error rate (EER) compared to non-finetuned baselines and an 83% decrease in training time relative to full fine-tuning, using the same training dataset. This approach provides a scalable and efficient solution for open-set RFF authentication in dynamic wireless vehicular networks.

翻译：射频指纹（RFFs）能够实现安全的无线认证，但在面临未知设备和变化信道的开放集场景中表现不佳。现有方法在泛化性方面面临挑战，且计算成本高昂。我们提出一种轻量级、自适应RFF提取框架，采用低秩适配（LoRA）技术。通过为每个环境预训练LoRA模块，我们的方法能够快速适应未见过的信道条件，而无需完整重训练。在推理过程中，LoRA模块的加权组合动态增强特征提取。实验结果表明，与未微调的基线相比，等错误率（EER）降低了15%；与完整微调相比，在相同训练数据集上训练时间减少了83%。该方法为动态无线车载网络中的开放集RFF认证提供了一种可扩展且高效的解决方案。