A Novel Semi-Supervised Learning Framework for Specific Emitter Identification

IEEE Vehicular Technology Conference, pp. 1–5

Abstract

Specific emitter identification (SEI) is developed as a potential technology against attackers in cognitive radio networks and authenticate devices in Internet of Things (IoT). It refers to a process to discriminate individual emitters from each other by analyzing extracted characteristics from given radio signals. Due to the strong capability of deep learning (DL) in extracting the hidden features of data and making classification decision, deep neural networks (DNNs) have been widely used in the SEI. Considering the insufficiently labeled training dataset and large unlabeled training dataset, we propose a novel SEI method using semi-supervised (SS) learning framework, i.e., metric-adversarial training (MAT). Specifically, two object functions (i.e., cross-entropy (CE) loss combined with deep metric learning (DML) and CE loss combined with virtual adversarial training (VAT)) and an alternating optimization way are designed to extract discriminative and generalized semantic features of radio signals. The proposed MAT-based SS-SEI method is evaluated on an open source large-scale real-world automatic-dependent surveillance-broadcast (ADS-B) dataset. The simulation results show that the proposed method achieves a better identification performance than four latest SS-SEI methods.

Authors 6

1. Xue Gang Fu

   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. Yu Wang

   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. 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

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

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

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

6. Fumiyuki Adachi

   Tohoku University

   Affiliation as printed

   Tohoku University,International Research Institute of Disaster Science (IRIDeS),Sendai,Japan

   International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai, Japan