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Main Authors:	Wu, Tuo, Pan, Cunhua, Zhi, Kangda, Ren, Hong, Elkashlan, Maged, Wang, Jiangzhou, Yuen, Chau
Format:	Preprint
Published:	2024
Subjects:	Signal Processing
Online Access:	https://arxiv.org/abs/2403.16521
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author	Wu, Tuo Pan, Cunhua Zhi, Kangda Ren, Hong Elkashlan, Maged Wang, Jiangzhou Yuen, Chau
author_facet	Wu, Tuo Pan, Cunhua Zhi, Kangda Ren, Hong Elkashlan, Maged Wang, Jiangzhou Yuen, Chau
contents	Reconfigurable intelligent surface (RIS)-aided localization systems have attracted extensive research attention due to their accuracy enhancement capabilities. However, most studies primarily utilized the base stations (BS) received signal, i.e., BS information, for localization algorithm design, neglecting the potential of RIS received signal, i.e., RIS information. Compared with BS information, RIS information offers higher dimension and richer feature set, thereby significantly improving the ability to extract positions of the mobile users (MUs). Addressing this oversight, this paper explores the algorithm design based on the high-dimensional RIS information. Specifically, we first propose a RIS information reconstruction (RIS-IR) algorithm to reconstruct the high-dimensional RIS information from the low-dimensional BS information. The proposed RIS-IR algorithm comprises a data processing module for preprocessing BS information, a convolution neural network (CNN) module for feature extraction, and an output module for outputting the reconstructed RIS information. Then, we propose a transfer learning based fingerprint (TFBF) algorithm that employs the reconstructed high-dimensional RIS information for MU localization. This involves adapting a pre-trained DenseNet-121 model to map the reconstructed RIS signal to the MU's three-dimensional (3D) position. Empirical results affirm that the localization performance is significantly influenced by the high-dimensional RIS information and maintains robustness against unoptimized phase shifts.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_16521
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Employing High-Dimensional RIS Information for RIS-aided Localization Systems Wu, Tuo Pan, Cunhua Zhi, Kangda Ren, Hong Elkashlan, Maged Wang, Jiangzhou Yuen, Chau Signal Processing Reconfigurable intelligent surface (RIS)-aided localization systems have attracted extensive research attention due to their accuracy enhancement capabilities. However, most studies primarily utilized the base stations (BS) received signal, i.e., BS information, for localization algorithm design, neglecting the potential of RIS received signal, i.e., RIS information. Compared with BS information, RIS information offers higher dimension and richer feature set, thereby significantly improving the ability to extract positions of the mobile users (MUs). Addressing this oversight, this paper explores the algorithm design based on the high-dimensional RIS information. Specifically, we first propose a RIS information reconstruction (RIS-IR) algorithm to reconstruct the high-dimensional RIS information from the low-dimensional BS information. The proposed RIS-IR algorithm comprises a data processing module for preprocessing BS information, a convolution neural network (CNN) module for feature extraction, and an output module for outputting the reconstructed RIS information. Then, we propose a transfer learning based fingerprint (TFBF) algorithm that employs the reconstructed high-dimensional RIS information for MU localization. This involves adapting a pre-trained DenseNet-121 model to map the reconstructed RIS signal to the MU's three-dimensional (3D) position. Empirical results affirm that the localization performance is significantly influenced by the high-dimensional RIS information and maintains robustness against unoptimized phase shifts.
title	Employing High-Dimensional RIS Information for RIS-aided Localization Systems
topic	Signal Processing
url	https://arxiv.org/abs/2403.16521

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