Publication result detail

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.

Original Title

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

English Title

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

Type

WoS Article

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

Released

16.06.2025

Publisher

IEEE

ISBN

2169-3536

Periodical

IEEE Access

Number

1

State

United States of America

Pages from

104844

Pages to

104857

Pages count

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"
}