An assessment of the impact of IQ imbalances on the physical layer authentication of IoT wireless devices

Physical Layer Authentication of wireless devices using their intrinsic physical features has been investigated in recent years by the research community. The concept is that small differences in the material and the composition of the electronic circuits of the wireless devices produce specific features in the Radio Frequency (RF) signal transmitted over the air. While these differences are usually not relevant to obstacle the correct functioning of wireless services, they are significant enough to uniquely identify the model or the electronic device itself once they are collected and processed by a RF receiver. Researchers have applied a variety of techniques to extract the features from the signal in space including statistical analysis and machine learning algorithms. In ideal conditions, the classification accuracy presented in the research literature is often higher than 95% but it can degrade significantly in the presence of non Line of Sight conditions. The research community has investigated the impact of low Signal to Noise (SNR) ratios or fading effects on the classification performance, but the disturbances introduced by the RF receiver itself have received little attention. In this paper, we investigate the impact of IQ imbalances of the RF receiver on the classification performance, which has not been attempted in the literature, yet. This impact is evaluated by means of the signals collected from 11 IoT wireless devices, using different representations of the signal for different values of the IQ imbalances.