  While the literature on RF fingerprinting-based authentication and key
distillation is vast, the two topics have customarily been studied separately.
In this paper, starting from the observation that the wireless channel is a
composite, deterministic / stochastic process, we propose a power domain
decomposition that allows performing the two tasks simultaneously. We devise
intelligent pre-processing schemes to decompose channel state information (CSI)
observation vectors into "predictable" and "unpredictable" components. The
former, primarily due to large-scale fading, can be used for node
authentication through RF fingerprinting. The latter, primarily due to
small-scale fading, could be used for semantically secure secret key generation
(SKG). To perform the decomposition, we propose: (i) a fingerprint
"separability" criterion, expressed through the maximisation of the total
variation distance between the empirical fingerprint measures; (ii) a
statistical independence metric for observations collected at different users,
expressed through a normalised version of the $d$-dimensional Hilbert Schmidt
independence criterion (dHSIC) test statistic. We propose both explicit
implementations, using principal component analysis (PCA) and kernel PCA and
black-box, unsupervised learning, using autoencoders. Our experiments on
synthetic and real CSI datasets showcase that the incorporation of RF
fingerprinting and SKG, with explicit security guarantees, is tangible in
future generations of wireless.
