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 ∼ 38\%. It also performs substantially better than other approaches that we have considered in terms of the distribution of error distance.
@article{arxiv.2511.08549,
title = {Vision Transformer Based User Equipment Positioning},
author = {Parshwa Shah and Dhaval K. Patel and Brijesh Soni and Miguel López-Benítez and Siddhartan Govindasamy},
journal= {arXiv preprint arXiv:2511.08549},
year = {2025}
}
Comments
The results are accepted in parts at IEEE CCNC2026