Identifying Wireless Users via Transmitter Imperfections

Abstract

Variations in the RF chain of radio transmitters can be used as a signature to uniquely associate wireless devices with a given transmission. Previous approaches, which have varied from transient analysis to machine learning, do not provide verifiable accuracy, which is essential for admissibility of the methods in the court. Here we detail a first step toward a model-based approach, which uses statistical models of RF transmitter components that are amenable for analysis. Algorithms based on statistical signal processing methods are developed to exploit non-linearities of wireless transmitters for the purpose of user identification in wireless systems. The decision rules are derived and their performance is analyzed. In order to establish the viability of the proposed approach, the practical variations of transmitter chain components are analyzed based on simulations, measurements and manufacturers' specifications. Results show that the proposed identification methods can be effective, even for short data records and relatively low signal-to-noise ratios, when exploiting imperfections of commercially used RF transmitters.