Wireless Physical-Layer Identification: Modeling and Validation

Abstract

Abstract-The wireless physical-layer identification (WPLI) techniques utilize the unique features of the physical waveforms of wireless signals to identify and classify authorized devices. As the inherent physical layer features are difficult to forge, WPLI is deemed as a promising technique for wireless security solutions. However, as of today it still remains unclear whether existing WPLI techniques can be applied under real-world requirements and constraints. In this paper, through both theoretical modeling and experiment validation, the reliability and differentiability of WPLI techniques are rigorously evaluated, especially under the constraints of state-of-art wireless devices, real operation environments, as well as wireless protocols and regulations. Specifically, a theoretical model is first established to systematically describe the complete procedure of WPLI. More importantly, the proposed model is then implemented to thoroughly characterize various WPLI techniques that utilize the spectrum features coming from the non-linear RF-front-end, under the influences from different transmitters, receivers, and wireless channels. Subsequently, the limitations of existing WPLI techniques are revealed and evaluated in details using both the developed theoretical model and in-lab experiments. The realworld requirements and constraints are characterized along each step in WPLI, including i) the signal processing at the transmitter (device to be identified), ii) the various physical layer features that originate from circuits, antenna, and environments, iii) the signal propagation in various wireless channels, iv) the signal reception and processing at the receiver (the identifier), and v) the fingerprint extraction and classification at the receiver.

Deep Learning Based Transmitter Identification using Power Amplifier Nonlinearity

“…The imperfections that enable this differentiation between transmitters arise from clock jitter, digital to analog converters, sampling errors, mixers or local frequency synthesizers, power amplifiers' non-linearity, device antennas, etc. The power amplifier's non-linearity is considered as the most significant source of differences [1].…”

Learning-Aided Physical Layer Authentication as an Intelligent Process

“…• C3. In practical wireless communication, the typical authentication schemes rely on nonlinear techniques, as exemplified by the binary hypothesis tests of [13]- [15] and by the generalized likelihood ratio test of [12];…”

An Analysis of the Privacy Threat in Vehicular Ad Hoc Networks due to Radio Frequency Fingerprinting

“…In practical scenarios as the one investigated in this paper in Section 4, these conditions are usually not met because a privacy attacker could be obliged to use less expensive equipment (like an Universal Software Radio Platform (USRP) Software Defined Radio (SDR)) and it would be subject to attenuation and fading effects because the DSRC signals must be tracked at a distance. Note that some papers have investigated the impact of White Gaussian Noise on the RF fingerprinting in a simulated way but not fading effects apart from the recent paper of [12] where a fading model has been applied but not to DSRC devices. As a consequence, a novel aspect of this paper is the application of fading models to RF fingerprinting for DSRC devices.…”