Towards location-awareness in next generation wireless networks : a new approach based on channel state information
Name
1191836271-MIT.pdf
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1.83 MB
Format
Adobe PDF
Checksum (MD5)
a0233e1f6dd7ea5707327b0dcda778a3
Author(s)
Yu, Zehao.
Advisor(s)
Moe Z. Win.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
Location-awareness in next generation wireless networks will be a key enabler for numerous emerging applications. Recently, a novel approach to localization based on soft information (SI), exploiting all positional information inherent in measurement and contextual data, has been proposed. This thesis further develops SI-based localization by establishing a new approach relying on channel state information (CSI) measurements. In particular, we design an efficient joint message-passing (MP) localization algorithm, which consists of two layers: the transformation layer and the estimation layer. The transformation layer extracts SI of the channel impulse response (CIR) from CSI measurements using a sparsity promoting prior model, which addresses the difficulty of unknown number of multipath in estimating the CIR. The estimation layer infers node positions based on the SI of the CIR using a delay-origin uncertainty model, which describes the conditional distribution of the delays in the CIR given node positions. Simulation results using QuaDriGa channel simulator show that our localization algorithm achieves decimeter-level localization accuracy for both Wi-Fi and mmWave signals, which outperforms conventional algorithms.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 69-78).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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