Feature extraction for channel reciprocity based secret key generation methods

Abstract

Channel Reciprocity-based Key Generation (CRKG) technique has gained significant attention among researchers in the field of Physical Layer Security (PLS). While existing methods in this area typically use raw channel information as input for secret key generation, we propose a novel approach that derives features from the raw material for key generation. Our comprehensive study explores a wide range of features derived from the reciprocal components of the Channel Impulse Response (CIR)s in both the time and frequency domains. Our findings demonstrate that the derived feature set exhibits better channel characteristics than the raw key material, even in the presence of eavesdroppers. We evaluate the efficiency of our proposed feature set using several performance metrics in a new feature-based key generation scheme to validate its efficiency. The results highlight the potential of this feature set for future key-generation applications.