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<p>Radio frequency fingerprinting (RFF) has emerged as a lightweight solution for wireless device authentication by leveraging unique hardware-induced signal variations. We propose an efficient discriminative feature extraction scheme directly from raw Wi-Fi signals, based on a Siamese Convolutional Neural Network (CNN) deep learning framework. The model is trained on structured triplet data to optimize inter-class separation and intra-class compactness. Without relying on complex preprocessing techniques, the proposed approach achieves 95.06% classification accuracy under a 5 dB signal-to-noise ratio (SNR), demonstrating its robustness in noisy environments. The results confirm the effectiveness of Siamese CNNs for the enhancement of physical-layer RFF in wireless networks.</p>

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Efficient feature extraction for radio frequency fingerprinting using Siamese CNN

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Sarkorjon Kurbonov,
  • Mutala Mohammed,
  • Zhi Chai,
  • Mingye Li,
  • Xuelin Yang

摘要

Radio frequency fingerprinting (RFF) has emerged as a lightweight solution for wireless device authentication by leveraging unique hardware-induced signal variations. We propose an efficient discriminative feature extraction scheme directly from raw Wi-Fi signals, based on a Siamese Convolutional Neural Network (CNN) deep learning framework. The model is trained on structured triplet data to optimize inter-class separation and intra-class compactness. Without relying on complex preprocessing techniques, the proposed approach achieves 95.06% classification accuracy under a 5 dB signal-to-noise ratio (SNR), demonstrating its robustness in noisy environments. The results confirm the effectiveness of Siamese CNNs for the enhancement of physical-layer RFF in wireless networks.