Neural Positioning Without External Reference
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915628576669696 |
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| author | Müller, Till-Yannic Zumegen, Frederik Wiesmayr, Reinhard Gönültaş, Emre Studer, Christoph |
| author_facet | Müller, Till-Yannic Zumegen, Frederik Wiesmayr, Reinhard Gönültaş, Emre Studer, Christoph |
| contents | Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hardware and renders label acquisition difficult in large areas. In this work, we propose a novel neural positioning pipeline that avoids the need for any external reference positioning system. Our approach trains the positioning network only using CSI acquired off-device and relative displacement commands executed on commercial off-the-shelf (COTS) robot platforms, such as robotic vacuum cleaners -- such an approach enables inexpensive training of accurate neural positioning functions over large areas. We evaluate our method in three real-world scenarios, ranging from small line-of-sight (LoS) areas to larger non-line-of-sight (NLoS) environments, using CSI measurements acquired in IEEE 802.11 Wi-Fi and 5G New Radio (NR) systems. Our experiments demonstrate that the proposed neural positioning pipeline achieves UE localization accuracies close to state-of-the-art methods that require externally acquired high-precision ground-truth position labels for training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Positioning Without External Reference Müller, Till-Yannic Zumegen, Frederik Wiesmayr, Reinhard Gönültaş, Emre Studer, Christoph Signal Processing Information Theory Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural networks with ground-truth position labels obtained from external reference positioning systems, which requires costly hardware and renders label acquisition difficult in large areas. In this work, we propose a novel neural positioning pipeline that avoids the need for any external reference positioning system. Our approach trains the positioning network only using CSI acquired off-device and relative displacement commands executed on commercial off-the-shelf (COTS) robot platforms, such as robotic vacuum cleaners -- such an approach enables inexpensive training of accurate neural positioning functions over large areas. We evaluate our method in three real-world scenarios, ranging from small line-of-sight (LoS) areas to larger non-line-of-sight (NLoS) environments, using CSI measurements acquired in IEEE 802.11 Wi-Fi and 5G New Radio (NR) systems. Our experiments demonstrate that the proposed neural positioning pipeline achieves UE localization accuracies close to state-of-the-art methods that require externally acquired high-precision ground-truth position labels for training. |
| title | Neural Positioning Without External Reference |
| topic | Signal Processing Information Theory |
| url | https://arxiv.org/abs/2511.16352 |