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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.09227 |
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| _version_ | 1866912704433750016 |
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| author | Mateos-Ramos, José Miguel Zumegen, Frederik Wymeersch, Henk Häger, Christian Studer, Christoph |
| author_facet | Mateos-Ramos, José Miguel Zumegen, Frederik Wymeersch, Henk Häger, Christian Studer, Christoph |
| contents | Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel method to produce CC locations in true spatial coordinates with the aid of a digital twin (DT). Our main contribution is a new framework that (i) extracts large-scale channel-state information (CSI) features from estimated CSI and the DT and (ii) matches these features with a cosine-similarity loss function. The DT-aided loss function is then combined with a conventional CC loss to learn a positioning function that provides true spatial coordinates without relying on labeled data. Our results for a simulated indoor scenario demonstrate that the proposed framework reduces the relative mean distance error by 29% compared to the state of the art. We also show that the proposed approach is robust to DT modeling mismatches and a distribution shift in the testing data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_09227 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features Mateos-Ramos, José Miguel Zumegen, Frederik Wymeersch, Henk Häger, Christian Studer, Christoph Signal Processing Channel charting (CC) is a self-supervised positioning technique whose main limitation is that the estimated positions lie in an arbitrary coordinate system that is not aligned with true spatial coordinates. In this work, we propose a novel method to produce CC locations in true spatial coordinates with the aid of a digital twin (DT). Our main contribution is a new framework that (i) extracts large-scale channel-state information (CSI) features from estimated CSI and the DT and (ii) matches these features with a cosine-similarity loss function. The DT-aided loss function is then combined with a conventional CC loss to learn a positioning function that provides true spatial coordinates without relying on labeled data. Our results for a simulated indoor scenario demonstrate that the proposed framework reduces the relative mean distance error by 29% compared to the state of the art. We also show that the proposed approach is robust to DT modeling mismatches and a distribution shift in the testing data. |
| title | Positioning via Digital-Twin-Aided Channel Charting with Large-Scale CSI Features |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.09227 |