Division-based Receiver-agnostic RFF Identification in WiFi Systems

TL;DR

This paper introduces a division-based method for WiFi radio frequency fingerprinting that is robust to receiver differences and channel variations, requiring only a single receiver for training and improving classification accuracy.

Contribution

The paper proposes a novel division-based RFF extraction technique that is receiver-agnostic and effective across different channel conditions, reducing the need for calibration.

Findings

Achieves 15.5% accuracy improvement in flat fading channels.

Achieves 28.45% accuracy improvement in frequency-selective fading channels.

Effectively mitigates receiver and channel effects in RFF identification.

Abstract

In physical-layer security schemes, radio frequency fingerprint (RFF) identification of WiFi devices is susceptible to receiver differences, which can significantly degrade classification performance when a model is trained on one receiver but tested on another. In this paper, we propose a division-based receiver-agnostic RFF extraction method for WiFi systems, which removes the receivers' effects by dividing different preambles in the frequency domain. The proposed method requires only a single receiver for training and does not rely on additional calibration or stacking processes. First, for flat fading channel scenarios, the legacy short training field (L-STF) and legacy long training field (L-LTF) of the unknown device are divided by those of the reference device in the frequency domain. The receiver-dependent effects can be eliminated with the requirement of only a single receiver for training, and the higher-dimensional RFF features can be extracted. Second, for frequency-selective fading channel scenarios, the high-throughput long training field (HT-LTF) is divided by the L-LTF in the frequency domain. Only a single receiver is required for training and the higher-dimensional RFF features that are both channel-invariant and receiver-agnostic are extracted. Finally, simulation and experimental results demonstrate that the proposed method effectively mitigate the impacts of channel variations and receiver differences. The classification results show that, even when training on a single receiver and testing on a different one, the proposed method achieves classification accuracy improvements of 15.5% and 28.45% over the state-of-the-art approach in flat fading and frequency-selective fading channel scenarios, respectively.