Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.20091 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914734154973184 |
|---|---|
| author | Zhao, Longhai Yang, Yunchuan Xiong, Qi Wang, He Yu, Bin Sun, Feifei Sun, Chengjun |
| author_facet | Zhao, Longhai Yang, Yunchuan Xiong, Qi Wang, He Yu, Bin Sun, Feifei Sun, Chengjun |
| contents | Channel charting, an unsupervised learning method that learns a low-dimensional representation from channel information to preserve geometrical property of physical space of user equipments (UEs), has drawn many attentions from both academic and industrial communities, because it can facilitate many downstream tasks, such as indoor localization, UE handover, beam management, and so on. However, many previous works mainly focus on charting that only preserves local geometry and use raw channel information to learn the chart, which do not consider the global geometry and are often computationally intensive and very time-consuming. Therefore, in this paper, a novel signature based approach for global channel charting with ultra low complexity is proposed. By using an iterated-integral based method called signature transform, a compact feature map and a novel distance metric are proposed, which enable channel charting with ultra low complexity and preserving both local and global geometry. We demonstrate the efficacy of our method using synthetic and open-source real-field datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_20091 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Signature Based Approach Towards Global Channel Charting with Ultra Low Complexity Zhao, Longhai Yang, Yunchuan Xiong, Qi Wang, He Yu, Bin Sun, Feifei Sun, Chengjun Information Theory Signal Processing Channel charting, an unsupervised learning method that learns a low-dimensional representation from channel information to preserve geometrical property of physical space of user equipments (UEs), has drawn many attentions from both academic and industrial communities, because it can facilitate many downstream tasks, such as indoor localization, UE handover, beam management, and so on. However, many previous works mainly focus on charting that only preserves local geometry and use raw channel information to learn the chart, which do not consider the global geometry and are often computationally intensive and very time-consuming. Therefore, in this paper, a novel signature based approach for global channel charting with ultra low complexity is proposed. By using an iterated-integral based method called signature transform, a compact feature map and a novel distance metric are proposed, which enable channel charting with ultra low complexity and preserving both local and global geometry. We demonstrate the efficacy of our method using synthetic and open-source real-field datasets. |
| title | A Signature Based Approach Towards Global Channel Charting with Ultra Low Complexity |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2403.20091 |