TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910188991152128 |
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| author | Ahadi, Mohsen Esrafilian, Omid Kaltenberger, Florian Malik, Adeel |
| author_facet | Ahadi, Mohsen Esrafilian, Omid Kaltenberger, Florian Malik, Adeel |
| contents | Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a TDoA-based self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluation of our proposed models in a real 5G testbed, benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN-based 5G network using OpenAirInterface (OAI) software at EURECOM. It demonstrates results that outperform state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2--4 meters in 90\% of cases, across varying NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper's evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_08001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation Ahadi, Mohsen Esrafilian, Omid Kaltenberger, Florian Malik, Adeel Networking and Internet Architecture Channel Charting (CC) has emerged as a promising framework for data-driven radio localization, yet existing approaches often struggle to scale globally and to handle the distortions introduced by non-line-of-sight (NLoS) conditions. In this work, we propose a novel CC method that leverages Channel Impulse Response (CIR) data enriched with practical features such as Time Difference of Arrival (TDoA) and Transmission Reception Point (TRP) locations, enabling a TDoA-based self-supervised localization function on a global scale. The proposed framework is further enhanced with short-interval User Equipment (UE) displacement measurements, which improve the continuity and robustness of the learned positioning function. Our algorithm incorporates a mechanism to identify and mask NLoS-induced noisy measurements, leading to significant performance gains. We present the evaluation of our proposed models in a real 5G testbed, benchmarked against centimeter-accurate Real-Time Kinematic (RTK) positioning, in an O-RAN-based 5G network using OpenAirInterface (OAI) software at EURECOM. It demonstrates results that outperform state-of-the-art semi-supervised and self-supervised CC approaches in a real-world scenario. The results show localization accuracies of 2--4 meters in 90\% of cases, across varying NLoS ratios. Furthermore, we provide public datasets of CIR recordings, along with the true position labels used in this paper's evaluation. |
| title | TDoA-Based Self-Supervised Channel Charting with NLoS Mitigation |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2510.08001 |