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Open AccessArticle

On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

1
Department of Communications Engineering, College of Electronic Technology, Bani Walid, Libya
2
Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
3
Department of Avionics, Atilim University, Kizilcasar Mah., 06830 Incek, Golbasi, Ankara, Turkey
4
Department of Electrical and Electronics Engineering, Atilim University, Kizilcasar Mah., 06830 Incek, Golbasi, Ankara, Turkey
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1704; https://doi.org/10.3390/s20061704
Received: 14 February 2020 / Revised: 11 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
(This article belongs to the Section Sensor Networks)
Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5–5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices. View Full-Text
Keywords: Bluetooth signals; feature extraction; RF fingerprinting; signal classification; emitter identification; variational mode decomposition Bluetooth signals; feature extraction; RF fingerprinting; signal classification; emitter identification; variational mode decomposition
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MDPI and ACS Style

Aghnaiya, A.; Dalveren, Y.; Kara, A. On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices. Sensors 2020, 20, 1704.

AMA Style

Aghnaiya A, Dalveren Y, Kara A. On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices. Sensors. 2020; 20(6):1704.

Chicago/Turabian Style

Aghnaiya, Alghannai; Dalveren, Yaser; Kara, Ali. 2020. "On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices" Sensors 20, no. 6: 1704.

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