RF-DNA Fingerprint Classification of OFDM Signals Using a Rayleigh Fading Channel Model

Abstract

The Internet of Things is a collection of Internet connected devices capable of interacting with the physical world and computer-based systems and is estimated to consist of 20 to 50 billion devices by the year 2020. Due to these numbers and the fact that 70% of the edge devices have no or poor encryption, there is a need for mechanisms by which to secure these devices and associated networks. Specific Emitter Identification (SEI) is one proposed mechanism for IoT security; however, performance within multipath environments has received little attention. This work presents the integration of a novel, Nelder-Mead (N-M)-based channel estimator within the Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting process to facilitate serial number discrimination when IoT devices are operating within a multipath environment and degrading Signal-to-Noise Ratio. Percent correct classification performance is used to assess the developed RF-DNA fingerprinting process. Two additional SEI approaches are assessed to facilitate comparative analysis of the serial number discrimination of four IEEE 802.11a Wi-Fi radios using a Rayleigh fading channel. Relative to the Adaptive Compensator (A-C) and N-M SEI approaches, RF-DNA fingerprinting provides the best means for achieving reliable (better than 95%) identification of all radios within a L=2 Rayleigh fading channel at SNR ≥ 21 dB.