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

arXiv: https://arxiv.org/abs/2604.12834 · HTML: https://mlsanity.com/paper/2604.12834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 14 Apr 2026.

## Builds on (most cited first, 9 of 9)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations](https://mlsanity.com/paper/2005.00954/) (2020)
- [Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa](https://mlsanity.com/paper/2107.02867/) (2021)
- [A Comprehensive Survey on Radio Frequency (RF) Fingerprinting: Traditional Approaches, Deep Learning, and Open Challenges](https://mlsanity.com/paper/2201.00680/) (2022)
- [Wireless Physical-Layer Identification: Modeling and Validation](https://mlsanity.com/paper/1510.08485/) (2015)
- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)

## Abstract (arXiv)

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.