Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Alghannai Aghnaiya,Aysha M Ali,Ali Kara

doi:10.1109/access.2019.2945121

Alghannai Aghnaiya, Aysha M Ali + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2945121

Copy DOI

Abstract

Radio frequency fingerprinting (RFF) is based on identification of unique features of RF transient signals emitted by radio devices. RF transient signals of radio devices are short in duration, non-stationary and nonlinear time series. This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD). For this purpose, VMD is used to decompose Bluetooth (BT) transient signals into a series of band-limited modes, and then, the transient signal is reconstructed from the modes. Higher order statistical (HOS) features are extracted from the complex form of reconstructed transients. Then, Linear Support Vector Machine (LVM) classifier is used to identify BT devices. The method has been tested experimentally with BT devices of different brands, models and series. The classification performance shows that VMD based RF fingerprinting method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as Hilbert-Huang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lower (2-3 dB) SNR levels.

Highlights

Radio frequency fingerprinting (RFF) is promising method for physical layer security in wireless networks
This paper evaluates the performance of RF fingerprinting method based on variational mode decomposition (VMD) for Bluetooth devices
Higher order statistical (HOS) features including variance, kurtosis and skewness are extracted from the reconstructed transient signals

Summary

INTRODUCTION

Radio frequency fingerprinting (RFF) is promising method for physical layer security in wireless networks. The values of the parameters in running the procedure have been obtained after examining related works [9]–[12], and experimental analysis as suggested in [9], [11] These are balancing parameter (α = 200), the number of modes (Z = 3), the tolerance of convergence (ε = 10−7), the initialization of center frequencies (ω0z = 0). In order to evaluate the performance of the RFF method under realistic noise conditions, different levels of captured channel noise were added randomly to the recorded transients that were captured initially at high Signal to Noise Ratio (SNR). For this purpose, three different datasets with varying SNR levels were created.

FEATURE EXTRACTION

CLASSIFICATION

Findings

CONCLUSION