Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917968653320192 |
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| author | Pan, Rui Chen, Hui Shen, Guanxiong Chen, Hongyang |
| author_facet | Pan, Rui Chen, Hui Shen, Guanxiong Chen, Hongyang |
| contents | In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean square error (MMSE) channel estimations followed by equalization, signals with different residual channel effects are generated. These residual channels enable the model to learn more effective representations. Then the pre-trained model is fine-tuned with 1% samples in a novel environment for RFFI. Experimental results demonstrate that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time, making it suitable for practical wireless security applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_08885 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification Pan, Rui Chen, Hui Shen, Guanxiong Chen, Hongyang Signal Processing Artificial Intelligence In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean square error (MMSE) channel estimations followed by equalization, signals with different residual channel effects are generated. These residual channels enable the model to learn more effective representations. Then the pre-trained model is fine-tuned with 1% samples in a novel environment for RFFI. Experimental results demonstrate that our method significantly enhances both feature extraction ability and generalization while requiring fewer samples and less time, making it suitable for practical wireless security applications. |
| title | Residual Channel Boosts Contrastive Learning for Radio Frequency Fingerprint Identification |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2412.08885 |