Device Authentication
This dataset contains LoRa physical layer signals collected from 60 LoRa devices and six SDRs (PLUTO-SDR, USRP B200 mini, USRP B210, USRP N210, RTL-SDR). It is intended for use by researchers in the development of a federated RFFI system, whereby the signals collected from different receivers and locations can be employed for evaluation purposes.
More details can be found at https://github.com/gxhen/federatedRFFI
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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.
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This project builds a length-versatile and noise-robust LoRa radio frequency fingerprint identification (RFFI) system. The LoRa signals are collected from 10 commercial-off-the-shelf LoRa devices, with the spreading factor (SF) set to 7, 8, 9, respectively. The packet preamble part and device labels are provided.
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This LoRa-RFFI project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techniques. The RF signals are collected from 60 commercial-off-the-shelf LoRa devices. The packet preamble part and device labels are provided. The dataset consists of 19 sub-datasets and please refer to the README document for more detailed collection settings for all the sub-datasets.
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