How to Design a Channel-Resilient Database for Radio Frequency Fingerprint Identification?

Abstract

This paper proposes to explore the Radio Frequency Fingerprint (RFF) identification with a virtual database generator. RFF is a unique signature created in the emitter transmission chain by hardware flaws. These flaws may be used as a secure identifier as they cannot be easily replicated for spoofing purposes. In recent years, the RFF identification relies mainly on Deep Learning (DL), and large databases are consequently needed to improve identification in different environmental conditions. In this paper, we introduce a virtual database and suggest utilizing it for the examination of three crucial aspects when creating a RFF database: the number of signals required to perform DL classification, the impact of RFF similarities between emitters, and the propagation channel impact in static and dynamic contexts. For instance, such analysis shows that data augmentation with 10 channels improves accuracy classification up to 70% in a scenario where RFFs are close from a transmitter to another.