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Employing High-Dimensional RIS Information for RIS-Aided Localization Systems
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DOI：10.1109/LCOMM.2024.3433517.png)
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摘要
En 中文
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 letter 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.
Keyword:
Location awareness
Feature extraction
Vectors
Image reconstruction
Accuracy
Data processing
Data mining
Reconfigurable intelligent surface (RIS)
localization
RIS information
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
Near-Field RSS-Based Localization Algorithms Using Reconfigurable Intelligent Surface基于可重构智能表面的近场RSS定位算法.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
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