Evaluating the Impact of Channel Conditions on Radio Frequency Fingerprint Identification Performance

Radio Frequency Fingerprint Identification (RFFI) has emerged as a promising solution, leveraging device-specific hardware imperfections in RF signals for secure and low-overhead authentication. While recent advances in deep learning have enhanced the performance of RFFI systems, their reliability in dynamic wireless environments remains a key challenge-particularly due to variations in channel conditions such as fading, noise levels, and mobility effects. This study presents a comprehensive simulation-based evaluation of RFFI robustness under varying channel parameters, including channel fading types (Rayleigh vs. Rician), signal-to-noise ratio (SNR), and Doppler Spread (DS). We systematically examine the impact of these individual factors, as well as mismatches between training and testing conditions, on the RFFI performance. The results reveal that Doppler-induced mobility poses the greatest challenge to RFFI performance, and that mismatches between training and testing environments can lead to substantial accuracy degradation. However, training under diverse and impaired conditions significantly improves model robustness, indicating that performance losses are not inherently irrecoverable.