RIS-Assisted Multi-User Localization in UAV-Enabled mmWave Wireless Networks

Localization techniques based on time of arrival (TOA) and angle of arrival (AOA) have been widely used in mobile communication systems and the localization accuracy can suffer from severe path loss and blockage. The mobility of UAVs and the signal redirection capabilities of reconfigurable intelligent surfaces (RISs) can be utilized to reduce these negative effects. In this paper, we propose a multi-user localization scheme in UAV-enabled millimeter-wave wireless networks, where the UAV localizes the chosen ground user by receiving the positioning reference signal from both the direct path and the reflected path via a RIS in each time instance. We derive the positioning error bound (PEB) based on the Fisher information matrix (FIM) of the unknown channel parameters and location parameters. Then we propose an alternating algorithm to minimize the maximum PEB among all users, by optimizing the UAV trajectory, the user scheduling, the UAV beamforming, and the RIS phase shifts iteratively until convergence. Furthermore, we formulate a robust optimization problem with imperfect knowledge of location parameters to minimize the maximum worst-case PEB, which can also be solved by the alternating algorithm. Numerical results show that the proposed alternating algorithm can improve the localization accuracy by more than twice compared to the scheme with a fixed base station and the scheme without RIS deployment.

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Publication Details

Journal
IEEE Transactions on Vehicular Technology
Published
2024-11-19
DOI
https://doi.org/10.1109/tvt.2024.3502155
Citations
6
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
Field-Weighted Citation Impact
1.25

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article

RIS-Assisted Multi-User Localization in UAV-Enabled mmWave Wireless Networks

Zesong Fei, Zheng Chang, Zhu Han, Jingwen Zhang et al.
6 citations
IEEE Transactions on Vehicular Technology
Indoor and Outdoor Localization Technologies
1.25
article

RIS-Assisted Multi-User Localization in UAV-Enabled mmWave Wireless Networks

Zesong Fei, Zheng Chang, Zhu Han, Jingwen Zhang, Zhong Zheng
article en
6 citations

Abstract

Localization techniques based on time of arrival (TOA) and angle of arrival (AOA) have been widely used in mobile communication systems and the localization accuracy can suffer from severe path loss and blockage. The mobility of UAVs and the signal redirection capabilities of reconfigurable intelligent surfaces (RISs) can be utilized to reduce these negative effects. In this paper, we propose a multi-user localization scheme in UAV-enabled millimeter-wave wireless networks, where the UAV localizes the chosen ground user by receiving the positioning reference signal from both the direct path and the reflected path via a RIS in each time instance. We derive the positioning error bound (PEB) based on the Fisher information matrix (FIM) of the unknown channel parameters and location parameters. Then we propose an alternating algorithm to minimize the maximum PEB among all users, by optimizing the UAV trajectory, the user scheduling, the UAV beamforming, and the RIS phase shifts iteratively until convergence. Furthermore, we formulate a robust optimization problem with imperfect knowledge of location parameters to minimize the maximum worst-case PEB, which can also be solved by the alternating algorithm. Numerical results show that the proposed alternating algorithm can improve the localization accuracy by more than twice compared to the scheme with a fixed base station and the scheme without RIS deployment.

IEEE Transactions on Vehicular TechnologyVol. 74(3)
Beijing Institute of Technology (CN), University of Electronic Science and Technology of China (CN), University of Houston (US)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 20%
Indoor and Outdoor Localization Technologies
1.25
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RIS-Assisted Multi-User Localization in UAV-Enabled mmWave Wireless Networks — Zesong Fei, Zheng Chang, et al. · IEEE Transactions on Vehicular Technology (2024) | TGRS Research Map | TGRS