# Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa

arXiv: https://arxiv.org/abs/2107.02867 · HTML: https://mlsanity.com/paper/2107.02867/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2021.

## Built on by (most cited first, 16 of 16)

- [Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics](https://mlsanity.com/paper/2208.02724/) (2022)
- [Edge AI-based Radio Frequency Fingerprinting for IoT Networks](https://mlsanity.com/paper/2412.10553/) (2024)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)
- [PRONTO: Preamble Overhead Reduction with Neural Networks for Coarse Synchronization](https://mlsanity.com/paper/2112.10885/) (2021)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)
- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)
- [Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting](https://mlsanity.com/paper/2509.15170/) (2025)
- [Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training](https://mlsanity.com/paper/2510.09405/) (2025)
- [Federated Radio Frequency Fingerprinting with Model Transfer and Adaptation](https://mlsanity.com/paper/2302.11418/) (2023)
- [VERITAS: Verifying the Performance of AI-native Transceiver Actions in Base-Stations](https://mlsanity.com/paper/2501.09761/) (2025)
- [MobRFFI: Non-cooperative Device Re-identification for Mobility Intelligence](https://mlsanity.com/paper/2503.02156/) (2025)
- [Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting](https://mlsanity.com/paper/2604.12834/) (2026)
- [Hamiltonian-Inspired Attention Mechanism for Scalable RF Transmitter Fingerprinting](https://mlsanity.com/paper/2605.30364/) (2026)
- [Cross-Receiver Open-Set Radio Frequency Fingerprinting via Structure-First Adaptation](https://mlsanity.com/paper/2607.02567/) (2026)
- [Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy](https://mlsanity.com/paper/2607.25070/) (2026)

## Abstract (arXiv)

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on the transmitter hardware impairments. In this paper, we propose a scalable and robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor. Specifically, we leverage the deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent feature and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for device classification and rogue device detection as well as effective channel mitigation.