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Paper Abstract and Keywords
Presentation 2023-05-19 09:15
An experimental evaluation of millimeter-wave link quality prediction using Wi-Fi CSI and supervised learning
Shoki Ohta, Kanare Kodera, Takayuki Nishio (Tokyo Tech) SeMI2023-10
Abstract (in Japanese) (See Japanese page) 
(in English) This study experimentally evaluates our 60 GHz band millimeter-wave (mmWave) link quality prediction method using 5 GHz band Wi-Fi channel state information (CSI) acquired at multiple locations and machine learning. Wireless communication using high-frequency waves such as mmWaves enables a high transmission rate due to its wide bandwidth. However, mmWave link quality significantly deteriorates when the mmWave line-of-sight (LOS) path is blocked by obstacles such as humans or vehicles. Existing studies have proposed methods that use computer vision information such as images or point clouds and machine learning to predict blockage, but there are many environments where acquiring images or point clouds is difficult due to privacy or cost concerns. In this study, we propose a method using 5 GHz band Wi-Fi CSI and supervised learning to predict the degradation of link quality due to LOS blockage. 5 GHz band CSI contains less privacy information compared to images or point clouds, and measurement devices are inexpensive and easy to install. However, CSI has difficulty understanding information about the position and movement of objects that block the mmWaveLOS path compared to computer vision information such as images and point clouds. We enabled a detailed understanding of mmWave propagation space information by installing CSI measurement devices in multiple locations. Experiment evaluations in indoor environments demonstrated that 5 GHz Wi-Fi CSI acquired from multiple locations can predict significant degradation of mmWave communication throughput 500 ms ahead.
Keyword (in Japanese) (See Japanese page) 
(in English) link quality prediction / millimeter-wave communication / CSI / machine learning / Wi-Fi / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 31, SeMI2023-10, pp. 42-45, May 2023.
Paper # SeMI2023-10 
Date of Issue 2023-05-11 (SeMI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SeMI IPSJ-ITS IPSJ-MBL IPSJ-DPS  
Conference Date 2023-05-18 - 2023-05-19 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Institute of Science and Technology (OIST) 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SeMI 
Conference Code 2023-05-SeMI-ITS-MBL-DPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An experimental evaluation of millimeter-wave link quality prediction using Wi-Fi CSI and supervised learning 
Sub Title (in English)  
Keyword(1) link quality prediction  
Keyword(2) millimeter-wave communication  
Keyword(3) CSI  
Keyword(4) machine learning  
Keyword(5) Wi-Fi  
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1st Author's Name Shoki Ohta  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Kanare Kodera  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
3rd Author's Name Takayuki Nishio  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
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Speaker Author-1 
Date Time 2023-05-19 09:15:00 
Presentation Time 15 minutes 
Registration for SeMI 
Paper # SeMI2023-10 
Volume (vol) vol.123 
Number (no) no.31 
Page pp.42-45 
#Pages
Date of Issue 2023-05-11 (SeMI) 


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