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Deep Learning based Cross-Receiver Radio Frequency Fingerprint Identification Under Varying Channels

Jiashuo He, Yumeng Wang, Feiyang He, Sai Huang, Yiheng Liu, Shuo Chang, Zhiyong Feng

TL;DR

This paper proposes a novel cross-receiver RFFI framework with channel robustness that enables robust transmitter classification on the target receiver under varying channel conditions and is the first work to jointly address channel and receiver portability through combined channel suppression and nonlinear receiver calibration.

Abstract

Radio frequency fingerprint identification (RFFI) exploits device-specific hardware impairments for transmitter recognition, but its performance is highly vulnerable to receiver variations and changing wireless channels in cross-receiver deployment. To address both challenges, this paper proposes a novel cross-receiver RFFI framework with channel robustness. In the enrollment stage, a channel-robust preprocessing method is developed to construct denoised spectral quotient (DSQ) sequences, and a DSQ-based convolutional neural network (DSQCNN) is trained using data collected from the source receiver. In the cross-receiver deployment stage, a calibration dataset is built from signals captured by both the source and target receivers, and a trainable calibration neural network (TCNN) is designed to learn the nonlinear mapping between them. The cascaded TCNN-DSQCNN framework then enables robust transmitter classification on the target receiver under varying channel conditions. To the best of our knowledge, this is the first work to jointly address channel and receiver portability through combined channel suppression and nonlinear receiver calibration. Simulations with twelve WiFi transmitters and three receivers show that the proposed method achieves reliable cross-receiver classification, reaching over 90\% accuracy at an SNR of 24 dB.

Deep Learning based Cross-Receiver Radio Frequency Fingerprint Identification Under Varying Channels

TL;DR

This paper proposes a novel cross-receiver RFFI framework with channel robustness that enables robust transmitter classification on the target receiver under varying channel conditions and is the first work to jointly address channel and receiver portability through combined channel suppression and nonlinear receiver calibration.

Abstract

Radio frequency fingerprint identification (RFFI) exploits device-specific hardware impairments for transmitter recognition, but its performance is highly vulnerable to receiver variations and changing wireless channels in cross-receiver deployment. To address both challenges, this paper proposes a novel cross-receiver RFFI framework with channel robustness. In the enrollment stage, a channel-robust preprocessing method is developed to construct denoised spectral quotient (DSQ) sequences, and a DSQ-based convolutional neural network (DSQCNN) is trained using data collected from the source receiver. In the cross-receiver deployment stage, a calibration dataset is built from signals captured by both the source and target receivers, and a trainable calibration neural network (TCNN) is designed to learn the nonlinear mapping between them. The cascaded TCNN-DSQCNN framework then enables robust transmitter classification on the target receiver under varying channel conditions. To the best of our knowledge, this is the first work to jointly address channel and receiver portability through combined channel suppression and nonlinear receiver calibration. Simulations with twelve WiFi transmitters and three receivers show that the proposed method achieves reliable cross-receiver classification, reaching over 90\% accuracy at an SNR of 24 dB.
Paper Structure (34 sections, 25 equations, 9 figures, 4 tables)

This paper contains 34 sections, 25 equations, 9 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Motivations
  3. Related Works
  4. Channel Portability
  5. Receiver Portability
  6. Channel and Receiver Portability
  7. Contributions
  8. Preliminaries
  9. Signal Model
  10. Problem Formulation
  11. The Proposed RFFI Protocol
  12. System Overview
  13. Operations in Enrollment Stage
  14. Operations in Cross-receiver Deployment Stage
  15. Channel-robust Signal Preprocessing Module
  16. ...and 19 more sections

Figures (9)

  • Figure 1: The generation flow chart of the WiFi signal impaired with coupled RFFs.
  • Figure 2: Legacy WiFi frame structure.
  • Figure 3: A typical example of an RFFI system's deployment considering channel and receiver portability.
  • Figure 4: The overall system of the proposed RFFI protocol.
  • Figure 5: Architecture of DSQCNN model.
  • ...and 4 more figures