WiFi RFFID: A Lightweight Temporal Convolutional Network Integrating Multi-scale and Channel Attention

TIAN Xinyu; ZHANG Xianshou; ZHENG Qinghe; ZHOU Fuhui; YU Lisu; HUANG Chongwen; JIANG Weiwei; SHU Feng; ZHAO Yizhe

doi:10.11999/JEIT260599

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TIAN Xinyu, ZHANG Xianshou, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. WiFi RFFID: A Lightweight Temporal Convolutional Network Integrating Multi-scale and Channel Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260599

Citation:

TIAN Xinyu, ZHANG Xianshou, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. WiFi RFFID: A Lightweight Temporal Convolutional Network Integrating Multi-scale and Channel Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260599

Citation:

TIAN Xinyu, ZHANG Xianshou, ZHENG Qinghe, ZHOU Fuhui, YU Lisu, HUANG Chongwen, JIANG Weiwei, SHU Feng, ZHAO Yizhe. WiFi RFFID: A Lightweight Temporal Convolutional Network Integrating Multi-scale and Channel Attention[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260599

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WiFi RFFID: A Lightweight Temporal Convolutional Network Integrating Multi-scale and Channel Attention

doi: 10.11999/JEIT260599 cstr: 32379.14.JEIT260599

1.
School of Intelligent Engineering, Shandong Management University, Jinan 250357, China
2.
School of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China
4.
School of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
5.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
6.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
7.
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Natural Science Foundation of China (62401070), The Shandong Provincial Natural Science Foundation (ZR2019ZD01, ZR2023QF125), The Shandong Provincial Youth Innovation Team Plan of Higher Education Institutions (2024KJH005), The Shandong Provincial Science and Technology Based Small and Medium sized Enterprises Innovation Capability Enhancement Project (2024TSGC0055)

Accepted Date: 2026-09-14
Rev Recd Date: 2026-09-14

Available Online: 2026-09-18

Abstract

Abstract

Objective With the widespread deployment of WiFi in the industrial Internet of Things (IoT), enterprise wireless local area networks, and smart homes, its security vulnerabilities have become increasingly prominent. The radio frequency fingerprint identification (RFFID) leverages hardware-level intrinsic variations introduced during manufacturing to authenticate devices, offering a robust solution for physical-layer security. However, existing RFFID methods often struggle with the trade-off between recognition robustness in dynamic channel environments and computational efficiency for deployment on resource-constrained platforms. This paper aims to develop a lightweight and efficient RFFID method that achieves high accuracy under varying channel conditions and device scales while maintaining low computational complexity suitable for embedded systems. Methods This paper proposes WiFi-RF-LTCN, a lightweight temporal convolutional network designed for WiFi RFFID. The method processes the legacy long training field (L-LTF) sequence from WiFi physical layer preamble. First, multi-dimensional features, including I/Q components, magnitude, and phase, are extracted to form a four-channel input. The core network architecture comprises two parallel branches: a temporal dilated convolution branch with residual connections to capture the long-range temporal dependencies across the signal, and a multi-scale learnable convolution branch utilizing kernels of varying sizes to enhance sensitivity to short-term local waveform distortions. Subsequently, a channel attention mechanism based on the squeeze-and-excitation block adaptively fuses features from both branches, highlighting discriminative fingerprints while suppressing redundant information. Finally, the model is trained and evaluated on a comprehensively simulated WiFi dataset incorporating diverse transmitter impairments and multipath channel effects. Results and Discussions Extensive experiments demonstrate that WiFi-RF-LTCN achieves the superior performance across various conditions. It attains high recognition accuracies of 93.12% at 0 dB SNR and 99.27% at 20 dB SNR. The model maintains robust performance even with limited training data (e.g., 82.21% accuracy with only 50 samples per device) and scales effectively as the number of devices increases. Ablation studies confirm the necessity of each component, with the complete model achieving average accuracy of 96.3% with the inference speed of 0.41 ms, significantly outperforming configurations missing any single module. Crucially, WiFi-RF-LTCN surpasses Transformer, ResNet50, TCN, 1D-CNN, and LSTM by 2.84%, 2.36%, 1.40%, 2.62%, and 3.61%, respectively. Moreover, it accomplishes this with only 0.17 M parameters and a remarkably low inference time, substantially reducing the computational overhead compared to larger models like ResNet50 (23.54 M) and Transformer (0.85 M) with inference time of 3.9381 ms and 0.8953 ms, respectively. Conclusions This paper presents a novel lightweight temporal convolutional network, WiFi-RF-LTCN, for WiFi device RFFID. By integrating multi-scale feature extraction, temporal modeling, and channel attention, the method effectively captures subtle hardware-induced distortions while demonstrating the strong robustness to noise and channel variations. The experimental results validate that WiFi-RF-LTCN achieves an optimal balance between high recognition accuracy and low computational cost, significantly outperforming existing deep learning methods. Its minimal parameter count and fast inference time make it highly suitable for real-time deployment on resource-constrained embedded platforms, offering the promising solution for enhancing physical-layer security in IoT and other wireless applications. Future work can further combine channel characteristics with adaptive optimization strategies to enhance the model’s generalization ability and deployment adaptability.
- Physical layer security,
- Equipment certification,
- Radio frequency fingerprint identification,
- Deep learning,
- Temporal convolutional network

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References(21)

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