Deep Learning - Powered Radio Frequency Fingerprint Identification: Methodology and Case Study

Shen, Guanxiong, Zhang, Junqing ORCID: 0000-0002-3502-2926 and Marshall, Alan ORCID: 0000-0002-8058-5242 (2023) Deep Learning - Powered Radio Frequency Fingerprint Identification: Methodology and Case Study. IEEE Communications Magazine, 61 (9). pp. 170-176.

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Abstract

Radio frequency fingerprint identification (RFFI) is an authentication technique that identifies wireless devices by analyzing the characteristics of the received physical layer signals. In recent years, RFFI has been significantly enhanced by deep learning. A neural network (NN) is often leveraged to predict device identity. As a data-driven approach, deep learning requires the collection of large amounts of data for NN training. In addition, the RFFI system should be evaluated on datasets collected under various conditions to assess the system's robustness. However, only a few RFFI datasets are publicly available, and there are no clear guidelines for building an RFFI testbed for data collection. This article presents a tutorial to build both closed-set and openset RFFI systems. A LoRa-RFFI testbed is created as a case study and the implementation details are described in depth. The LoRa-RFFI testbed involves 60 commercial-off-the-shelf (COTS) LoRa development boards as devices to be identified, and a USRP N210 software-defined radio (SDR) platform for physical layer signal reception. Experiments are carried out using the implemented LoRa-RFFI testbed, and the collected datasets are made publicly available online. It is anticipated that this work can aid the research community in constructing RFFI testbeds and facilitate the development of RFFI research.

Item Type:	Article
Additional Information:	(c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	10 Mar 2023 08:56
Last Modified:	08 Nov 2023 09:04
DOI:	10.1109/mcom.001.2200695
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168923