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

Received Date: 2026-05-14
Accepted Date: 2026-09-14
Rev Recd Date: 2026-09-12

Available Online: 2026-09-18

Abstract

Abstract

Objective With the widespread deployment of WiFi in industrial Internet of Things (IoT), enterprise wireless local area networks, and smart home scenarios, security vulnerabilities have become increasingly prominent. Radio Frequency Fingerprint IDentification (RFFID) leverages hardware-induced variations introduced during manufacturing to authenticate devices, providing an effective solution for physical-layer security. However, existing RFFID methods often face challenges in balancing recognition robustness under dynamic channel conditions and computational efficiency for deployment on resource-constrained platforms. This paper aims to develop a lightweight and efficient RFFID method that achieves high recognition accuracy under varying channel conditions and device scales while maintaining low computational complexity for embedded systems. Methods This paper proposes WiFi-RF-LTCN, a lightweight temporal convolutional network for WiFi RFFID. The method processes the Legacy Long Training Field (L-LTF) sequence from the WiFi physical-layer preamble. First, multi-dimensional features, including I/Q components, amplitude, and phase, are extracted to form a four-channel input. The proposed network consists of two parallel branches: a temporal dilated convolution branch with residual connections to capture long-range temporal dependencies in the signal, and a multi-scale learnable convolution branch with different kernel 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 RF fingerprint features while suppressing redundant information. Finally, the model is trained and evaluated on a simulated WiFi dataset incorporating diverse transmitter impairments and multipath channel effects. Results and Discussions Extensive experiments demonstrate that WiFi-RF-LTCN achieves superior performance under various conditions. It achieves recognition accuracy of 93.12% at 0 dB Signal-to-Noise Ratio (SNR) and 99.27% at 20 dB SNR. The model maintains robust performance with limited training data, achieving an accuracy of 82.21% with only 50 samples per device, and exhibits good scalability as the number of devices increases. Ablation experiments verify the effectiveness of each component, with the complete model achieving an average accuracy of 96.30% with an inference time of 0.41 ms. Compared with Transformer, ResNet50, TCN, 1D-CNN, and LSTM, WiFi-RF-LTCN achieves accuracy improvements of 2.84, 2.36, 1.40, 2.62, and 3.61 percentage points, respectively. Moreover, it requires only 0.17 M parameters, substantially reducing computational overhead compared with ResNet50 (23.54 M) and Transformer (0.85 M), whose inference times are 3.9381 ms and 0.8953 ms, respectively. Conclusions This paper presents a lightweight temporal convolutional network, WiFi-RF-LTCN, for WiFi device identification using RFFID. By integrating multi-scale feature extraction, temporal modeling, and channel attention, the proposed method effectively captures subtle hardware-induced distortions and demonstrates strong robustness to noise and channel variations. Experimental results verify that WiFi-RF-LTCN achieves a favorable balance between recognition accuracy and computational cost, outperforming existing deep learning methods. Its low parameter count and short inference time make it suitable for real-time deployment on resource-constrained embedded platforms, providing an effective solution for enhancing physical-layer security in IoT and other wireless applications.
- Physical layer security,
- Device authentication,
- Radio frequency fingerprint identification,
- Deep learning,
- Temporal convolutional network

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References(21)

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