Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles

Lina Sheng, Yao Xu, Yan Li (), Yang Yang and Nan Fu
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Lina Sheng: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Yao Xu: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Yan Li: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Yang Yang: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China
Nan Fu: School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China

Mathematics, 2025, vol. 13, issue 19, 1-19

Abstract: As a typical representative of complex networks, the Internet of Vehicles (IoV) is more vulnerable to malicious attacks due to the mobility and complex environment of devices, which requires a secure and efficient authentication mechanism. Radio frequency fingerprinting (RFF) presents a novel research perspective for identity authentication within the IoV. However, as device fingerprint features are directly extracted from wireless signals, their stability is significantly affected by variations in the communication channel. Furthermore, the interplay between wireless channels and receiver noise can result in the distortion of the received signal, complicating the direct separation of the genuine features of the transmitted signals. To address these issues, this paper proposes a method for RFF extraction based on the physical sidelink broadcast channel (PSBCH). First, necessary preprocessing is performed on the signal. Subsequently, the wireless channel, which lacks genuine features, is estimated using linear minimum mean square error (LMMSE) techniques. Meanwhile, the previous statistical models of the channel and noise are incorporated into the analysis process to accurately capture the channel distortion caused by multipath effects and noise. Ultimately, the impact of the channel is mitigated through a channel-equalization operation to extract fingerprint features, and identification is carried out using a structurally optimized ShuffleNet V2 network. Based on a lightweight design, this network integrates an attention mechanism that enables the model to adaptively concentrate on the most distinguishable weak features in low signal-to-noise ratio (SNR) conditions, thereby enhancing the robustness of feature extraction. The experimental results show that in fixed and mobile scenarios with low SNR, the classification accuracy of the proposed method reaches 96.76% and 91.05%, respectively.

Keywords: complex networks; internet of vehicles; radio frequency fingerprint; identification learning; linear minimum mean square error; ShuffleNet V2 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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