Beyond Static Signatures: Statistical Analysis of Radio Fingerprint Mutations

Radio Frequency Fingerprinting (RFF) has emerged as a promising physical-layer technique for device identification, leveraging the hardware imperfections in radio transmitters. However, the assumption that RF fingerprints are static and persistent is increasingly challenged by recent findings. In this work, we present a comprehensive statistical analysis of radio fingerprint mutations, focusing on the impact of FPGA image reloads in Software Defined Radios (SDRs) when used as both transmitters and receivers. Our results highlight that FPGA reloads cause a subset of devices to exhibit two different persistent fingerprint states, one following a memoryless (Markovian) process and the other retaining temporal dependencies. Notably, we show that proper external synchronization between transmitter and receiver eliminates these fingerprint mutations, leading us to attribute the phenomenon to residual phase and timing errors rather than inherent hardware changes. Our work exposes the necessity of accounting for fingerprint dynamics caused by internal SDR events and synchronization, highlighting the limits of current measurement methodologies and the need for new, statistically robust approaches to physical-layer device identification.