On the Impact of CDL and TDL Augmentation for RF Fingerprinting under Impaired Channels

TL;DR

The paper addresses RF fingerprinting under impaired channels, highlighting the sensitivity of transmitter-specific fingerprints to channel conditions and the challenge of collecting diverse training data. It evaluates CDL+TDL augmentation-based deep learning, including 5G-only-CDL and WiFi-only-TDL variants, using a dataset spanning 5G, 4G, and WiFi to boost generalization on unobserved conditions. Results show CDL+TDL achieves accuracy of 87.59% on unobserved data, 81.63% for 5G-only-CDL, and 79.21% for WiFi-only-TDL, outperforming baseline TDL/CDL augmentation (77.81%) and no augmentation (74.84%). The findings demonstrate that augmentation techniques can significantly enhance RF fingerprinting robustness in realistic deployment scenarios without extensive new data collection.

Abstract

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.