Radio Frequency Fingerprint Identification for LoRa Using Deep Learning

Shen, Guanxiong; Zhang, Junqing; Marshall, Alan; Peng, Linning; Wang, Xianbin

doi:10.1109/jsac.2021.3087250

Cited by 128 publications

(53 citation statements)

References 37 publications

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“…Many previous deep learning-based RFFI schemes lack scalability [20], [23], [29], [38], [45]. More specifically, it neither supports efficient device joining and leaving nor rogue device detection ability.…”

Section: Related Workmentioning

confidence: 99%

“…The received signal needs to be pre-processed to meet the basic requirements of RFFI, including synchronization, carrier frequency offset (CFO) compensation and normalization. These algorithms are briefly described below and detailed descriptions can be found in our prior work [38].…”

Section: Preprocessingmentioning

confidence: 99%

“…In our prior work, we explored the selection of signal representation (IQ samples, FFT coeefficients and spectrogram), stability, and deep learning models for RFFI systems [38]. However, the above mentioned challenges, i.e., the scalability and channel robustness, are not well addressed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Shen

Zhang

Marshall

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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141

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

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

Shen

Zhang

Marshall

et al. 2022

IEEE Trans.Inform.Forensic Secur.

Self Cite

141

View full text Add to dashboard Cite

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“…Recently, the rich characteristics of the physical layer have been intensively investigated to implement device identification in wireless networks [4][5][6], also known as physical layer identification. Various physical layer features can be extracted and performed as the device's identity.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach takes advantage of the powerful learning ability of deep learning to identify wireless devices with the collected raw in-phase and quadrature (IQ) signal or its transformed information [6,[12][13][14][15][16][17][18][19]. Hence, it is known as deep learning-based physical layer identification.…”

Section: Introductionmentioning

confidence: 99%

On the Performance Supremum of CFO Based Physical Layer Identification

Zeng

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Physical layer identification is an emerging technique that exploits physical layer features to identify wireless devices. The identification accuracy and the device quantity that can be identified at most are significant for the identification scheme. Existing works primarily focus on the feature correlation analysis for multifeature selection without investigating the least upper bound (supremum) of the performance of a single feature. The supremum indicates the limit of the performance, which is another sight for evaluating the quality of features and improving the performance of the identification scheme. Therefore, this paper first investigates the supremum of the performance of the most commonly used physical layer feature, i.e., carrier frequency offset (CFO). Specifically, we offer a rigorous mathematical analysis and derive the closed-form expression of the supremum of identification accuracy based on the max-min distance analysis (MMDA) criterion. And then, the supremum of the number of distinguishable devices is also analyzed. Finally, we conducted a simulation study to verify the theoretical analysis result.

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