Vision Transformer Based User Equipment Positioning
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912702304092160 |
|---|---|
| author | Shah, Parshwa Patel, Dhaval K. Soni, Brijesh López-Benítez, Miguel Govindasamy, Siddhartan |
| author_facet | Shah, Parshwa Patel, Dhaval K. Soni, Brijesh López-Benítez, Miguel Govindasamy, Siddhartan |
| contents | Recently, Deep Learning (DL) techniques have been used for User Equipment (UE) positioning. However, the key shortcomings of such models is that: i) they weigh the same attention to the entire input; ii) they are not well suited for the non-sequential data e.g., when only instantaneous Channel State Information (CSI) is available. In this context, we propose an attention-based Vision Transformer (ViT) architecture that focuses on the Angle Delay Profile (ADP) from CSI matrix. Our approach, validated on the `DeepMIMO' and `ViWi' ray-tracing datasets, achieves an Root Mean Squared Error (RMSE) of 0.55m indoors, 13.59m outdoors in DeepMIMO, and 3.45m in ViWi's outdoor blockage scenario. The proposed scheme outperforms state-of-the-art schemes by $\sim$ 38\%. It also performs substantially better than other approaches that we have considered in terms of the distribution of error distance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08549 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vision Transformer Based User Equipment Positioning Shah, Parshwa Patel, Dhaval K. Soni, Brijesh López-Benítez, Miguel Govindasamy, Siddhartan Computer Vision and Pattern Recognition Networking and Internet Architecture Recently, Deep Learning (DL) techniques have been used for User Equipment (UE) positioning. However, the key shortcomings of such models is that: i) they weigh the same attention to the entire input; ii) they are not well suited for the non-sequential data e.g., when only instantaneous Channel State Information (CSI) is available. In this context, we propose an attention-based Vision Transformer (ViT) architecture that focuses on the Angle Delay Profile (ADP) from CSI matrix. Our approach, validated on the `DeepMIMO' and `ViWi' ray-tracing datasets, achieves an Root Mean Squared Error (RMSE) of 0.55m indoors, 13.59m outdoors in DeepMIMO, and 3.45m in ViWi's outdoor blockage scenario. The proposed scheme outperforms state-of-the-art schemes by $\sim$ 38\%. It also performs substantially better than other approaches that we have considered in terms of the distribution of error distance. |
| title | Vision Transformer Based User Equipment Positioning |
| topic | Computer Vision and Pattern Recognition Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.08549 |