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Delay Estimation for Ranging and Localization Using Multiband Channel State Information

delete2022-04-01
delete27
delete
OA
AI
T
Tarik Kazaz *
G
Gerard J. M. Janssen
J
Jac Romme
A
A. van Veen
DOI:10.1109/TWC.2021.3113771delete
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摘要

摘要

En 中文
In wireless networks, an essential step for precise range-based localization is the high-resolution estimation of multipath channel delays. The resolution of traditional delay estimation algorithms is inversely proportional to the bandwidth of the training signals used for channel probing. Considering that typical training signals have limited bandwidth, delay estimation using these algorithms often leads to poor localization performance. To mitigate these constraints, we exploit the multiband and carrier frequency switching capabilities of wireless transceivers and propose to acquire channel state information (CSI) in multiple bands spread over a large frequency aperture. The data model of the acquired measurements has a multiple shift-invariance structure, and we use this property to develop a high-resolution delay estimation algorithm. We derive the Cramer-Rao Bound (CRB) for the data model and perform numerical simulations of the algorithm using system parameters of the emerging IEEE 802.11be standard. Simulations show that the algorithm is asymptotically efficient and converges to the CRB. To validate modeling assumptions, we test the algorithm using channel measurements acquired in real indoor scenarios. From these results, it is seen that delays (ranges) estimated from multiband CSI with a total bandwidth of 320 MHz show an average RMSE of less than 0.3 ns (10 cm) in 90% of the cases.
Keyword:
Estimation
Delay estimation
OFDM
Location awareness
Training
Frequency estimation
Data models
Delay estimation
ranging
super resolution
subspace fitting
multiband CSI
IEEE 802
11be
WiFi-7
OFDM

期刊

IEEE Transactions on Wireless Communications 封面图
IEEE Transactions on Wireless Communications
IF:
10.7
论文数:
1.3W
被引数:
5.3W

机构

D
Delft University of Technology
学者数:
2.6W
论文数: 2.5W
被引数: 3.8W
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