Detail publikačního výsledku

Robust Radio Frequency Fingerprinting with Signal Denoising and Stacked Multivariate Ensemble Learning for Secure Wireless Communications

USMAN ALI SHAH, S.; USAMA ZAHID, M.; ABUZAR H SHAH, S.; REHMAN, S.; KOMOSNÝ, D.

Originální název

Robust Radio Frequency Fingerprinting with Signal Denoising and Stacked Multivariate Ensemble Learning for Secure Wireless Communications

Anglický název

Robust Radio Frequency Fingerprinting with Signal Denoising and Stacked Multivariate Ensemble Learning for Secure Wireless Communications

Druh

Článek WoS

Originální abstrakt

Radio Frequency Fingerprinting (RFF) has gained significant attention in wireless communication and security research due to its potential for device authentication and intrusion detection. While deep learning-based approaches have shown promising results, existing methods suffer from critical limitations: high computational complexity hinders real-time deployment on resource-constrained hardware, poor robustness under low Signal-to-Noise Ratio (SNR) conditions, and inadequate generalization across different datasets. To address these gaps, this paper proposes a novel and efficient RFF framework that integrates signal denoising preprocessing using Savitzky-Golay Filtering (SGF) with Stacked Multivariate Ensemble Learning (SMvEL). The proposed architecture employs lightweight, homogeneous Convolutional Neural Networks (CNNs) optimized for rapid model training and fast inference, ensuring computational efficiency without sacrificing accuracy. Experimental results on real-world walkie-talkie datasets, as well as two open-source benchmark datasets for cellphones and drones, demonstrate that the proposed method outperforms state-of-the-art deep learning approaches in both accuracy and robustness.

Anglický abstrakt

Radio Frequency Fingerprinting (RFF) has gained significant attention in wireless communication and security research due to its potential for device authentication and intrusion detection. While deep learning-based approaches have shown promising results, existing methods suffer from critical limitations: high computational complexity hinders real-time deployment on resource-constrained hardware, poor robustness under low Signal-to-Noise Ratio (SNR) conditions, and inadequate generalization across different datasets. To address these gaps, this paper proposes a novel and efficient RFF framework that integrates signal denoising preprocessing using Savitzky-Golay Filtering (SGF) with Stacked Multivariate Ensemble Learning (SMvEL). The proposed architecture employs lightweight, homogeneous Convolutional Neural Networks (CNNs) optimized for rapid model training and fast inference, ensuring computational efficiency without sacrificing accuracy. Experimental results on real-world walkie-talkie datasets, as well as two open-source benchmark datasets for cellphones and drones, demonstrate that the proposed method outperforms state-of-the-art deep learning approaches in both accuracy and robustness.

Klíčová slova

Convolutional Neural Network (CNN);Device Identification;Deep Learning;Ensemble Learning;Radio Frequency Fingerprinting (RFF);Specific Emitter Identification (SEI)

Klíčová slova v angličtině

Convolutional Neural Network (CNN);Device Identification;Deep Learning;Ensemble Learning;Radio Frequency Fingerprinting (RFF);Specific Emitter Identification (SEI)

Autoři

USMAN ALI SHAH, S.; USAMA ZAHID, M.; ABUZAR H SHAH, S.; REHMAN, S.; KOMOSNÝ, D.

Rok RIV

2026

Vydáno

16.06.2025

Nakladatel

IEEE

ISSN

2169-3536

Periodikum

IEEE Access

Číslo

1

Stát

Spojené státy americké

Strany od

104844

Strany do

104857

Strany počet

14

URL

https://ieeexplore.ieee.org/document/11034694

BibTex

@article{BUT198151,
  author="Syed {Usman Ali Shah} and Muhammad {Usama Zahid} and Syed {Abuzar H Shah} and Saeed {Rehman} and Dan {Komosný}",
  title="Robust Radio Frequency Fingerprinting with Signal Denoising and Stacked Multivariate Ensemble Learning for Secure Wireless Communications",
  journal="IEEE Access",
  year="2025",
  number="1",
  pages="104844--104857",
  issn="2169-3536",
  url="https://ieeexplore.ieee.org/document/11034694"
}