Work

Transformer-based localization in UAV-RIS enabled non-terrestrial networks

Year: 2025

Type: article

Abstract: 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. © 2025 The Author(s). Published by Elsevier B.V. on behalf of The Korean Institute of Communications and Information Sciences. This is an open-access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).

Source: ICT Express

DOI: 10.1016/j.icte.2025.11.017

Authors: Seungseok Sin, Sangmi Moon, Cheol Hong Kim, Intae Hwang

Institutions: Chonnam National University, Korea Nazarene University, Soongsil University

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,500