Radio Frequency Fingerprint LoRa Dataset With Multiple Receivers

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. The receiver hardware impairments interfere with the feature extraction of transmitter impairments, but their effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, collaborative inference are designed to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for underperforming receivers. The system is further evaluated on a more practical testbed. By making additional use of online augmentation and multi-packet inference, the identification accuracy is improved from 50% to 90% at 10 dB

搜集汇总

背景与挑战

背景概述

该数据集是一个用于射频指纹识别（RFFI）研究的LoRa数据集，包含10个LoRa设备和20个SDR接收器的信号数据，旨在支持接收器无关和协作推理方法的开发。数据格式为HDF5，适用于人工智能、物联网和安全性领域的研究，特别是设备认证和硬件特征提取。