Power Amplifier enabled RF Fingerprint Identification

Yuepei Li, Yuan Ding, George Goussetis, Junqing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

The development of radio frequency fingerprint (RFF) identification technique aims to identify and classify wireless devices by exploiting their unique radio frequency (RF) features. This paper proposes a wireless device RFF identification scheme, which relies on the non-linear memory effect resulting from the concatenation of two matched root-raised cosine (RRC) filters with a non-linear power amplifier (PA) inserted in-between. This unique feature can be extracted from the distorted constellation diagrams, which is processed into density trace figure. Classification algorithm based on image recognition method is developed to exploit the non-linear memory effect from the density trace figure. Simulation setup is carefully designed, and the results validated the effectiveness of our proposed RFF feature classification approach. In the range of SNR 1 dB to 25 dB, the overall identification accuracy rate exceeds 99%.
Original languageEnglish
Title of host publication2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS)
PublisherIEEE
ISBN (Electronic)9781665403092
DOIs
Publication statusPublished - 27 Jul 2021
Event2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems - , United States
Duration: 18 May 202120 May 2021
https://texassymposium.org/

Conference

Conference2021 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems
Country/TerritoryUnited States
Period18/05/2120/05/21
Internet address

Keywords

  • Convolution neural network (CNN)
  • power amplifier non-linearity
  • radio frequency fingerprint (RFF)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Instrumentation

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