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

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 219
• Main Authors: Jagannath, Anu, Jagannath, Jithin, Kumar, Prem Sagar Pattanshetty Vasanth
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 24.12.2022
• Subjects: Deep learning; Emitter identification; Radio fingerprinting; Signal and modulation classification; Signal intelligence; Wireless security; Deep learning; Signal intelligence; Wireless security; Signal and modulation classification; Radio fingerprinting; Emitter identification
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2022.109455

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.