A Machine Learning Approach for Hierarchical Localization Based on Multipath MIMO Fingerprints

Jiancun Fan; Susu Chen; Xinmin Luo; Ying Zhang; Geoffrey Ye Li

doi:10.1109/LCOMM.2019.2929148

A Machine Learning Approach for Hierarchical Localization Based on Multipath MIMO Fingerprints

Jiancun Fan
, Susu Chen
, Xinmin Luo
, Ying Zhang
, Geoffrey Ye Li

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is developed for hierarchical localization exploiting multipath MIMO fingerprints. By using multipath MIMO CSI in the time domain, instead in the frequency domain, our hierarchical scheme uses coarse-to-fine process and can achieve accurate positioning, which is demonstrated by simulation results.

Original language	English
Article number	8764436
Pages (from-to)	1765-1768
Number of pages	4
Journal	IEEE Communications Letters
Volume	23
Issue number	10
DOIs	https://doi.org/10.1109/LCOMM.2019.2929148
State	Published - Oct 2019

Keywords

Machine learning
hierarchical localization
multipath MIMO fingerprints
softmax regression classifier

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10.1109/LCOMM.2019.2929148

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title = "A Machine Learning Approach for Hierarchical Localization Based on Multipath MIMO Fingerprints",

abstract = "Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is developed for hierarchical localization exploiting multipath MIMO fingerprints. By using multipath MIMO CSI in the time domain, instead in the frequency domain, our hierarchical scheme uses coarse-to-fine process and can achieve accurate positioning, which is demonstrated by simulation results.",

keywords = "Machine learning, hierarchical localization, multipath MIMO fingerprints, softmax regression classifier",

author = "Jiancun Fan and Susu Chen and Xinmin Luo and Ying Zhang and Li, \{Geoffrey Ye\}",

note = "Publisher Copyright: {\textcopyright} 1997-2012 IEEE.",

year = "2019",

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AU - Luo, Xinmin

AU - Zhang, Ying

AU - Li, Geoffrey Ye

PY - 2019/10

Y1 - 2019/10

N2 - Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is developed for hierarchical localization exploiting multipath MIMO fingerprints. By using multipath MIMO CSI in the time domain, instead in the frequency domain, our hierarchical scheme uses coarse-to-fine process and can achieve accurate positioning, which is demonstrated by simulation results.

AB - Localization based on channel state information (CSI) fingerprints in multiple-input multiple-output (MIMO) systems is one of the most promising positioning technologies. In this letter, a machine learning approach is developed for hierarchical localization exploiting multipath MIMO fingerprints. By using multipath MIMO CSI in the time domain, instead in the frequency domain, our hierarchical scheme uses coarse-to-fine process and can achieve accurate positioning, which is demonstrated by simulation results.

KW - Machine learning

KW - hierarchical localization

KW - multipath MIMO fingerprints

KW - softmax regression classifier

UR - https://www.scopus.com/pages/publications/85073519746

U2 - 10.1109/LCOMM.2019.2929148

DO - 10.1109/LCOMM.2019.2929148

M3 - 文章

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JO - IEEE Communications Letters

JF - IEEE Communications Letters

IS - 10

M1 - 8764436

ER -

A Machine Learning Approach for Hierarchical Localization Based on Multipath MIMO Fingerprints

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Keywords

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