Variational Mode Decomposition Based Radio Frequency Fingerprinting Of Bluetooth Devices | Atılım Üniversitesi Açık Erişim Sistemi
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Variational Mode Decomposition Based Radio Frequency Fingerprinting Of Bluetooth Devices
Diğer Başlık: Varyasyonel Kip Ayrıştırıcı Kullanarak Bluetooth Cihazların Radyo Frekans Parmak İzi Çıkarımı
Oluşturulma Tarihi: 06-10-2020
Niteleme Bilgileri
Tür: Tez
Alt Tür: Doktora
Yayınlanma Durumu: Yayınlanmamış
Dosya Biçimi: PDF
Dil: İngilizce
Konu(lar): Elektrik mühendisliği. Elektronik. Nükleer mühendislik,
Yazar(lar): Aghanaiya, Alghannai M. Arhouma (Yazar),
Emeği Geçen(ler): Kara, Ali (Tez Danışmanı),
URL: http://acikarsiv.atilim.edu.tr/browse/2532/
Diğer Niteleme Bilgileri: http://acikarsiv.atilim.edu.tr/browse/2532/10226429.pdf
Variational mode decomposition, Bluetooth signals, Specific emitter identification, Feature extraction, Classification.
In this thesis, we evaluated the performance of RF fingerprinting method based on variational mode decomposition (VMD). 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. 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 VMD-reconstructed transients and VMD-modes. 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 VMDreconstructed transients method achieves better performance (at least 8% higher) than time-frequency-energy (TFED) distribution based methods such as HilbertHuang Transform. This is demonstrated with the same dataset but with smaller number of features (nine features) and slightly lowers (2-3 dB) SNR levels. For the same dataset the classification performance demonstrates that VMD-modes method achieves better performance (4% higher) than VMD-reconstructed transient method.
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