A Novel Radio Frequency Fingerprint Identification Method Using Incremental Learning

IEEE Vehicular Technology Conference, pp. 1–5

Abstract

Radio frequency fingerprint (RFF) is regarded as a key technology in physical layer security in various wireless communications systems. Deep learning (DL) has achieved great success in the field of signal identification, particularly in improving performance and eliminating manual feature extraction. However, the training cost of these DL-based methods is usually large. It is unwise to retrain the network with whole data when it comes to new data. Therefore, we propose a novel RFF identification method based on incremental learning (IL), which uses continuous data stream to update the identification model, constantly. Experimental results show that with the increase of increment times, the accuracy of the proposed IL-based method gradually approaches the performance of joint training, and finally reaches 96.79%, which is only 1.9% lower than the performance upper bound.

Authors 7

1. Jie Zhou

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   NJUPT,College of Telecommunications and Information Engineering,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

2. Yang Peng

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   NJUPT,College of Telecommunications and Information Engineering,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

3. Guan Gui

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   NJUPT,College of Telecommunications and Information Engineering,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China

4. Yun Lin

   Harbin Engineering University

   Affiliation as printed

   Harbin Engineering University,College of Information and Communication Engineering,Harbin,China

   College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

5. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Department of Engineering,Manchester,UK

   Department of Engineering, Manchester Metropolitan University, Manchester, UK

6. Haris Gacanin Aachen Institute for Communication Technologies and Embedded Systems

   RWTH Aachen University

   Affiliation as printed

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University,Aachen,Germany

   Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Aachen, Germany

7. Hikmet Sari

   Nanjing University of Posts and Telecommunications

   Affiliation as printed

   NJUPT,College of Telecommunications and Information Engineering,Nanjing,China

   College of Telecommunications and Information Engineering, NJUPT, Nanjing, China