Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting

2026-04-14

AI Summary

This work addresses the poor generalization and high computational cost of open-set radio frequency (RF) fingerprint authentication under unknown devices and dynamic channel conditions. To this end, it introduces Low-Rank Adaptation (LoRA)—a technique previously unexplored in this domain—and proposes a lightweight adaptive fingerprint extraction framework. The method leverages environment-specific LoRA modules pretrained offline and dynamically aggregates them via weighted fusion during inference, enabling rapid channel adaptation and feature enhancement without full-model fine-tuning. Experimental results demonstrate that, on the same dataset, the proposed approach reduces the equal error rate by 15% compared to non-fine-tuned baselines while cutting training time by 83% relative to full fine-tuning.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• open-set radio frequency fingerprinting
• wireless channel adaptation
• unknown devices
• dynamic wireless networks
• generalization

Innovation

• LoRA
• radio frequency fingerprinting
• open-set recognition
• channel adaptation
• lightweight adaptation