Specific Emitter Identification Based on Radio Frequency Fingerprint Using Multi-Scale Network

IEEE Vehicular Technology Conference, pp. 1–5

Abstract

The fast development of intelligent wireless communications enables many devices to access various networks. It often leads to the security risks of malicious access of illegal devices. To ensure a secure and reliable wireless access, it is necessary to identify illegal devices and prevent their attacks accurately. To improve the performance of specific emitter identification (SEI), this paper proposes a multi-scale convolution neural network (MSCNN) based on convolution layers of three branches with different convolution kernel sizes. MSCNN extracts radio frequency fingerprints (RFF) in three receptive fields through different convolution kernels. We verify the identification accuracy using the RF signals conforming to long term evolution (LTE) standard. The experimental results show that our proposed MSCNN-based SEI method can improve the absolute accuracy by 15% and the relative accuracy by 22% in perfect communication environment. In addition, we verify the robustness of proposed MSCNN by comparing identification performance in imperfect environment. Simulation results show that the proposed MSCNN can extract more hidden features through convolution kernels of different sizes, and thus achieves better SEI performance than existing methods.

Authors 6

1. Yibin Zhang

   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. Bamidele Adebisi

   Manchester Metropolitan University

   Affiliation as printed

   Manchester Metropolitan University,Department of Engineering,Manchester,UK

   Department of Engineering, Manchester Metropolitan University, Manchester, UK

4. 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

5. 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

6. 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