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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Online-Zugang: | https://arxiv.org/abs/2605.15593 |
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| _version_ | 1866911687727120384 |
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| author | Razak, Fawaz Abdul Yilmaz, Yasin |
| author_facet | Razak, Fawaz Abdul Yilmaz, Yasin |
| contents | Radio Frequency Fingerprinting (RFF) using deep learning has gained attention as a complementary approach to cryptographic authentication, offering resistance to spoofing, replay attacks, and key leakage. While most RFF approaches rely on In-Phase and Quadrature (IQ) samples, Channel State Information (CSI) has emerged as a more accessible alternative, enabling device authentication through physical-layer characteristics. In this work, we propose ContraCSI, a CSI-based contrastive learning framework for RFF using low-cost ESP32 devices. We investigate multiple encoder backbones, including a Vision Transformer (ViT), a lightweight 3D-CNN (Lite3D-CNN), and R3D18, to learn joint CSI and device-ID embeddings for transmitter authentication. For closed-set identification, the ViT variants achieve the best overall performance. We further study open-set authentication by applying a Geometric Entropy Minimization (GEM)-based anomaly score and sequential CUSUM (Cumulative Sum) test on embeddings learned by Lite3D-CNN-Contra, enabling rejection of unseen or non-enrolled transmitters rather than forcing a closed-set label. To evaluate robustness in highly dynamic and crowded indoor environments with human motion, multipath fading, and varying device orientations and distances, we conduct extensive experiments in a real-world setting. Our results demonstrate high authentication accuracy, strong generalization in non-ideal conditions, and effective rejection of unknown transmitters. Additionally, we explore CSI-based indoor localization via trilateration, illustrating the potential for integrated authentication and localization in practical indoor deployments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15593 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Dynamic and Open-Set RF Fingerprinting and Localization in Crowded Indoor Environments through Contrastive Channel State Information Learning Razak, Fawaz Abdul Yilmaz, Yasin Signal Processing Radio Frequency Fingerprinting (RFF) using deep learning has gained attention as a complementary approach to cryptographic authentication, offering resistance to spoofing, replay attacks, and key leakage. While most RFF approaches rely on In-Phase and Quadrature (IQ) samples, Channel State Information (CSI) has emerged as a more accessible alternative, enabling device authentication through physical-layer characteristics. In this work, we propose ContraCSI, a CSI-based contrastive learning framework for RFF using low-cost ESP32 devices. We investigate multiple encoder backbones, including a Vision Transformer (ViT), a lightweight 3D-CNN (Lite3D-CNN), and R3D18, to learn joint CSI and device-ID embeddings for transmitter authentication. For closed-set identification, the ViT variants achieve the best overall performance. We further study open-set authentication by applying a Geometric Entropy Minimization (GEM)-based anomaly score and sequential CUSUM (Cumulative Sum) test on embeddings learned by Lite3D-CNN-Contra, enabling rejection of unseen or non-enrolled transmitters rather than forcing a closed-set label. To evaluate robustness in highly dynamic and crowded indoor environments with human motion, multipath fading, and varying device orientations and distances, we conduct extensive experiments in a real-world setting. Our results demonstrate high authentication accuracy, strong generalization in non-ideal conditions, and effective rejection of unknown transmitters. Additionally, we explore CSI-based indoor localization via trilateration, illustrating the potential for integrated authentication and localization in practical indoor deployments. |
| title | Dynamic and Open-Set RF Fingerprinting and Localization in Crowded Indoor Environments through Contrastive Channel State Information Learning |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.15593 |