Channel-Resilient Deep-Learning-Driven Device Fingerprinting Through Multiple Data Streams

Abstract

Enabling accurate and automated identification of wireless devices is critical for allowing network access monitoring and ensuring data authentication for large-scale IoT networks. RF fingerprinting has emerged as a solution for device identification by leveraging the transmitters’ inevitable hardware impairments that occur during manufacturing. Although deep learning is proven efficient in classifying devices based on hardware impairments, the performance of deep learning models suffers greatly from variations of the wireless channel conditions, across time and space. To the best of our knowledge, we are the first to propose leveraging MIMO capabilities to mitigate the channel effect and provide a channel-resilient device classification framework. We begin by showing that for AWGN channels, combining multiple received signals improves the testing accuracy by up to 30%. We then show that for more realistic Rayleigh channels, blind channel estimation enabled by MIMO increases the testing accuracy by up to 50% when the models are trained and tested over the same channel, and by up to 69% when the models are tested on a channel that is different from that used for training.