  Cyber-physical systems have recently been used in several areas (such as
connected and autonomous vehicles) due to their high maneuverability. On the
other hand, they are susceptible to cyber-attacks. Radio frequency (RF)
fingerprinting emerges as a promising approach. This work aims to analyze the
impact of decoupling tapped delay line and clustered delay line (TDL+CDL)
augmentation-driven deep learning (DL) on transmitter-specific fingerprints to
discriminate malicious users from legitimate ones. This work also considers
5G-only-CDL, WiFi-only-TDL augmentation approaches. RF fingerprinting models
are sensitive to changing channels and environmental conditions. For this
reason, they should be considered during the deployment of a DL model. Data
acquisition can be another option. Nonetheless, gathering samples under various
conditions for a train set formation may be quite hard. Consequently, data
acquisition may not be feasible. This work uses a dataset that includes 5G, 4G,
and WiFi samples, and it empowers a CDL+TDL-based augmentation technique in
order to boost the learning performance of the DL model. Numerical results show
that CDL+TDL, 5G-only-CDL, and WiFi-only-TDL augmentation approaches achieve
87.59%, 81.63%, 79.21% accuracy on unobserved data while TDL/CDL augmentation
technique and no augmentation approach result in 77.81% and 74.84% accuracy on
unobserved data, respectively.
