Review

Radio frequency fingerprint identification for Internet of Things: A survey

¹ School of Cyber Science and Engineering, Southeast University, Nanjing, 210096, China
² Purple Mountain Laboratories for Network and Communication Security, Nanjing, 211111, China
³ Department of Electrical Engineering and Electronics, University of Liverpool L69 3GJ, Liverpool, UK
⁴ School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

^* Corresponding author (email: pengln@seu.edu.cn)

Received: 28 April 2023
Revised: 12 July 2023
Accepted: 2 August 2023

Abstract

Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing, and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination, and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based, and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning, and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.