Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Yuepei Li, Yuan Ding, Junqing Zhang, George Goussetis, Symon Podilchak

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
159 Downloads (Pure)

Abstract

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.
Original languageEnglish
Pages (from-to)1618-1631
Number of pages14
JournalIEEE Transactions on Cognitive Communications and Networking
Volume8
Issue number4
Early online date6 Oct 2022
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Communication system security
  • Convolution neural network (CNN)
  • Feature extraction
  • Radio frequency
  • Signal to noise ratio
  • Symbols
  • Wireless communication
  • Wireless sensor networks
  • non-linear memory effect
  • radio frequency fingerprint (RFF)

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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