A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Abstract

Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with the spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL)-based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect – background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues – necessary to elucidate this topic to the reader in an encyclopedic manner.