  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.
