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Transformer-Based Localization in UAV-RIS enabled Non-Terrestrial Networks
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DOI：10.1016/j.icte.2025.11.017.png)
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Abstract
En 中文
Accurate localization is essential for next-generation wireless systems. Traditional millimeter-wave (mmWave) techniques rely heavily on line-of-sight (LOS) paths, which limits their performance in non-line-of-sight (NLOS) environments. To overcome this challenge, we propose a non-terrestrial network (NTN) framework that employs an unmanned aerial vehicle–mounted reconfigurable intelligent surface (UAV-RIS) in conjunction with a Transformer-based refinement model. Unlike conventional regression or filtering approaches, the Transformer leverages self-attention mechanisms to refine coarse geometric estimates. Simulations using the DeepMIMO dataset show that more than 90% of users achieve sub-meter localization accuracy, representing a 35% improvement over existing baselines. These results demonstrate the novelty and effectiveness of integrating RIS adaptability with Transformer-based learning to enable robust, high-precision localization.
Keywords:
Deep learning
line-of-sight (LOS)
localization
non-terrestrial network (NTN)
reconfigurable intelligent surface (RIS)
Transformer
unmanned aerial vehicle (UAV)
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