Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics

TL;DR

This paper introduces a disentangled representation learning framework for RF fingerprint extraction that improves generalization to unknown devices and environments by separating device-specific features from channel effects using adversarial training.

Contribution

It proposes a novel disentangled representation learning approach with implicit data augmentation to prevent overfitting to channel statistics in RF fingerprinting.

Findings

Outperforms conventional methods in generalizing to unknown devices.

Effective under complex propagation environments like multipath fading.

Achieves robust RF fingerprinting without extensive data collection from diverse channels.

Abstract

Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation~(MLE). Although their discriminability has recently been extended to unknown devices in open-set scenarios, they still tend to overfit the channel statistics embedded in the training dataset. This restricts their practical applications as it is challenging to collect sufficient training data capturing the characteristics of all possible wireless channel environments. To address this challenge, we propose a DL framework of disentangled representation~(DR) learning that first learns to factor the signals into a device-relevant component and a device-irrelevant component via adversarial learning. Then, it shuffles these two parts within a dataset for implicit data augmentation, which imposes a strong regularization on RFF extractor learning to avoid the possible overfitting of device-irrelevant channel statistics, without collecting additional data from unknown channels. Experiments validate that the proposed approach, referred to as DR-based RFF, outperforms conventional methods in terms of generalizability to unknown devices even under unknown complicated propagation environments, e.g., dispersive multipath fading channels, even though all the training data are collected in a simple environment with dominated direct line-of-sight~(LoS) propagation paths.