Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics. (arXiv:2208.02724v1 [eess.SP])

Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF)
has attracted significant attention in physical-layer authentications due to
its extraordinary classification performance. Conventional DL-RFF techniques,
trained by adopting maximum likelihood estimation~(MLE), tend to overfit the
channel statistics embedded in the training dataset. This restricts their
practical applications as it is challenging to collect sufficient training data
capturing the characteristics of all possible wireless channel environments. To
address this challenge, we propose a DL framework of disentangled
representation learning~(DRL) that first …

channel fingerprint representation rf statistics under