2026-04-17T05:27:07Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4277912026-02-07T09:47:28Zcom_2072_1033col_2072_452950

RIS phase optimization for near-field 5G positioning: ML-enhanced CRLB minimization Macías López, Carla Saumell i Portillo, Arnau Nájar Martón, Montserrat Closas Gómez, Pau Training Location awareness Accuracy 5G mobile communication Optimization methods Line-of-sight propagation Machine learning Reconfigurable intelligent surfaces Signal processing Minimization This article addresses near-field localization using Reconfigurable Intelligent Surfaces (RIS) in 5G systems, where Line-of-Sight (LOS) between the base station and the user is obstructed. We propose a RIS phase optimization method based on the minimization of the Cramér-Rao Lower Bound (CRLB). This minimization itself is computationally costly, for which a data-driven method is employed with remarkable computational savings and positioning performance. The main contributions of this work are: (1) the application of machine learning (ML) to enhance CRLB minimization for RIS phase optimization; (2) an overview on RIS phases preprocessing methods to enhance deep neural networks training for the task; and (3) an end-to-end simulation of the positioning task with the presented method, showing a computational improvement without compromising positioning accuracy. The UPC authors are within the Signal Processing and Communications Group at UPC recognized as a consolidated research group by the Generalitat de Catalunya through 2021 SGR 01033. This publication is part of the project ROUTE56 with grant PID2019-104945GB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and the project 6-SENSES with grant PID2022-138648OB-I00 funded by MCIN/AEI/ 10.13039/501100011033 and by ERDF A way of making Europe. This work has been partially supported by the National Science Foundation under Awards ECCS-1845833 and CCF-2326559. Peer Reviewed Postprint (author's final draft) 2024 Conference report https://ieeexplore.ieee.org/document/10715164 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104945GB-I00/ES/TECNOLOGIAS RADIO PARA COMUNICACIONES UBICUAS EN LA EVOLUCION DE 5G A 6G/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-138648OB-I00/ES/COMUNICACIONES 6G Y SENSADO PARA REDES INALAMBRICAS DETERMINISTAS/ Open Access Institute of Electrical and Electronics Engineers (IEEE)