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

TL;DR

This paper introduces a novel deep learning framework for radio frequency fingerprint identification that remains accurate across different receivers and channel conditions by combining channel-robust preprocessing, a DSQ-based CNN, and a trainable calibration network.

Contribution

It presents the first integrated approach to address both receiver variability and channel changes in RF fingerprint identification using deep learning.

Findings

Achieves over 90% accuracy at 24 dB SNR in simulations.

Develops a channel-robust preprocessing method for spectral data.

Introduces a trainable calibration network for cross-receiver adaptation.

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.