Smart Channel State Information Pre-Processing for Authentication and Symmetric Key Distillation

While the literature on channel state information (CSI)-based authentication and key distillation is vast, the two topics have customarily been studied separately. This paper proposes unsupervised learning techniques to disentangle deterministic from stochastic fading to decompose observed CSI vectors into “predictable” and “unpredictable” components. The former, primarily due to large-scale fading, can be used for node authentication. The latter, primarily due to small-scale fading, can be used for secret key generation (SKG). The parameterization of the decomposition is performed using the following metrics: 1) CSI fingerprint “separability” criterion, expressed through the maximisation of the total variation distance (TVD) between the empirical CSI fingerprints; 2) statistical independence metric for CSI collected at different users in neighboring locations, using the d-dimensional Hilbert Schmidt independence criterion (dHSIC) test statistic; and 3) estimation of information leakage at different users to determine the amount of necessary hashing for privacy amplification in the SKG using the FBLAEU machine learning based conditional min-entropy estimator. Employing principal component analysis (PCA), kernel PCA and autoencoders on synthetic and natural CSI datasets, this work shows that explicit security guarantees can be provided by using physical layer security for authentication and key agreement.