Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles
Abstract
1. Introduction
- (1)
- A novel RFF extraction method based on LMMSE channel estimation is proposed. This method optimizes the channel response estimation by combining the channel covariance matrix and noise statistical information and removes the channel response to generate initial fingerprint features, improving the robustness of fingerprint extraction in complex environments.
- (2)
- The ShuffleNet V2 network architecture is optimized for low SNR and dynamic channel conditions through the integration of an attention mechanism module. This approach can preserve high feature-extraction capabilities while maintaining a lightweight network, enhancing the model’s adaptability and generalization performance to ensure stable identification results.
- (3)
- During the training process, data collected from diverse scenarios is integrated to enhance the model’s generalization ability. By increasing the informational richness of the features, both computational load and the number of parameters are effectively reduced. This strategy improves computational efficiency while ensuring detection accuracy, thereby achieving a lightweight network. Experimental validation demonstrates the effectiveness of the proposed method, achieving classification accuracies of 96.76% and 91.05% in low-SNR stationery and mobile scenarios, respectively.
2. Related Work
3. System Overview
3.1. PSBCH
3.2. System Framework
4. Data Preprocessing
4.1. Signal Detection
4.2. Frame Synchronization
4.3. CFO Compensation
4.4. Resource Grid Demodulation
5. Fingerprint Extraction and Classification
5.1. Channel Estimation and Equalization
5.2. Improved ShuffleNet V2 Network
5.2.1. Network Architecture Design
5.2.2. Attention Mechanism
6. Results and Discussion
6.1. Experimental Environment
6.2. Evaluation Criteria
6.3. Experimental Results and Discussion
6.3.1. Classification Under Different SNR
6.3.2. Classification in Different Scenarios
6.3.3. Comparison of Different Models
6.3.4. Model-Authentication Performance Evaluation
- (1)
- Experimental Setup
- (2)
- Authentication Method
- (3)
- Performance Metrics
- (4)
- Evaluation Criteria
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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(Method/Approach) | Advantages | Limitations |
---|---|---|
Transient Features [11,23] | No data demodulation required. | Significant performance degradation on persistent symbols. |
CFO Features [12,13] | Simple concept, channel estimation-free. | Insufficient stability and inter-class separability. |
Adversarial Training [15]/Disentanglement [16] | Data-driven, automatic decoupling. | Risk of “over-purifying” and losing RFF details. |
Data Augmentation [17,18] | Enhances model’s tolerance to variations. | Increases model training overhead. |
Noise Injection [19] | Leverages physical principles, no preamble needed. | Mismatches with the physical model of the actual channel. |
Protocol-Specific Compensation [20,21] | Performs well within its native system. | Relies on specific frame structures. |
Channel Reciprocity [22] | Enhances model’s tolerance to variations. | Requires stacking of multiple consecutive frames from the same transmitter. |
LS Channel Estimation [24] | Simple to implement, direct channel removal. | Performance degrades under low SNR. |
Training Set | ACC/F1 (%) | ||||
---|---|---|---|---|---|
LOS | NLOS | MOV1 | MOV2 | MOV3 | |
DC | 99.75/99.73 | 98.82/98.56 | 99.01/98.05 | 98.09/98.51 | 99.59/99.59 |
DC + LOS | 100/100 | 99.96/99.94 | 99.28/98.23 | 99.41/99.51 | 100/100 |
DC + NLOS | 95.64/95.06 | 100/100 | 99.89/99.92 | 96.51/97.35 | 99.67/99.67 |
DC + MOV1 | 100/100 | 99.32/99.33 | 100/100 | 97.72/98.6 | 99.87/99.87 |
DC + MOV2 | 99.96/99.97 | 99.82/99.8 | 98.73/97.19 | 100/100 | 99.96/99.96 |
DC + MOV3 | 100/100 | 99.58/99.67 | 99.6/99.57 | 99.54/99.63 | 100/100 |
LOS | 100/100 | 99.28/98.74 | 98.07/98.19 | 95.68/95.71 | 99.87/99.85 |
LOS + NLOS | 99.99/99.99 | 99.99/99.97 | 99.98/99.98 | 98.27/98.12 | 100/100 |
LOS + MOV1 | 100/100 | 98.99/99.16 | 100/100 | 98.89/98.92 | 99.93/99.91 |
LOS + MOV2 | 100/100 | 99.28/99.4 | 99.92/99.92 | 100/100 | 100/100 |
LOS + MOV3 | 100/100 | 98.27/98.68 | 99.24/99.48 | 95.74/95.22 | 100/100 |
Model | Overall Accuracy (%) | Macro Precision (%) | Macro F1 (%) |
---|---|---|---|
DenseNet | 95.19 | 93.15 | 93.92 |
MobileNet | 92.7 | 88.93 | 88.12 |
EfficientNet | 96.63 | 97.02 | 96.24 |
MobileVit | 86.47 | 85.08 | 84.22 |
ConvNeXt | 96.72 | 94 | 95.43 |
ShuffleNet V2 | 96.74 | 95.02 | 95.19 |
Ours | 99.01 | 97.44 | 98.05 |
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Sheng, L.; Xu, Y.; Li, Y.; Yang, Y.; Fu, N. Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles. Mathematics 2025, 13, 3124. https://doi.org/10.3390/math13193124
Sheng L, Xu Y, Li Y, Yang Y, Fu N. Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles. Mathematics. 2025; 13(19):3124. https://doi.org/10.3390/math13193124
Chicago/Turabian StyleSheng, Lina, Yao Xu, Yan Li, Yang Yang, and Nan Fu. 2025. "Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles" Mathematics 13, no. 19: 3124. https://doi.org/10.3390/math13193124
APA StyleSheng, L., Xu, Y., Li, Y., Yang, Y., & Fu, N. (2025). Radio Frequency Fingerprint-Identification Learning Method Based-On LMMSE Channel Estimation for Internet of Vehicles. Mathematics, 13(19), 3124. https://doi.org/10.3390/math13193124