用轻量LoRA快速适配未知信道，提升无线指纹认证效率
Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting
- 基于预训练的LoRA模块，动态加权组合实现快速适配
- 相比未微调基线，等错误率降低15%，训练时间减少83%
- 适合车联网等动态信道场景下的实时无线认证
射频指纹（RFF）可实现安全无线认证，但在开放集场景下面对未知设备和变化信道时表现不佳。现有方法存在泛化能力差与计算成本高的问题。本文提出一种轻量级、自适应的RFF提取框架，采用低秩适配（LoRA）。通过为每个环境预训练LoRA模块，该方法可在不进行完整重训练的情况下快速适应未知信道条件。推理时，通过加权组合多个LoRA动态增强特征提取能力。实验表明，在相同训练数据集上，相比非微调基线，等错误率（EER）降低15%；相比全参数微调，训练时间减少83%。该方法为动态无线车载网络中的开放集RFF认证提供了高效可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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