Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth. (arXiv:2210.02897v1 [cs.NI])

A scalable and computationally efficient framework is designed to fingerprint
real-world Bluetooth devices. We propose an embedding-assisted attentional
framework (Mbed-ATN) suitable for fingerprinting actual Bluetooth devices. Its
generalization capability is analyzed in different settings and the effect of
sample length and anti-aliasing decimation is demonstrated. The embedding
module serves as a dimensionality reduction unit that maps the high dimensional
3D input tensor to a 1D feature vector for further processing by the ATN
module. Furthermore, unlike the prior research in …

arxiv bluetooth deep learning embedding