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Paper Abstract and Keywords
Presentation 2024-06-07 11:10
Machine Learning Dataset Based on Omnidirectional Images for mmWave Multipath Propagation Prediction for Indoor-Short-Range Communication
Masaki Oda, Yusuke Koda, Norichika Ohmi, Hiroshi Harada (Kyoto Univ.) SRW2024-5 MWPTHz2024-5
Abstract (in Japanese) (See Japanese page) 
(in English) This report proposes a machine learning dataset based on camera images for millimeter wave (mmWave) multipath propagation prediction and evaluates its effectiveness. In mmWave communications using highly directional antennas, the best performance should be achieved and maintained by directing their main lobe in the direction of a path with higher power and without other delayed multipaths. On the other hand, in order for user equipment to measure its multipath characteristics based only on radio frequency (RF) signals, it is generally necessary to perform a channel estimation while rotating the directional antenna, which causes a large signaling overhead. In this paper, we propose a computer vision-aided system concept that learns to estimate and predict mmWave multipath characteristics based on omnidirectional images without performing the RF signaling- based channel estimation. Then, we evaluate and discuss the effectiveness of the machine learning dataset consisting of 7200 samples in total, which were constructed from power delay profiles obtained at different receiver positions, antenna angles, and person placement, as well as omnidirectional images obtained in the aforementioned test setting.
Keyword (in Japanese) (See Japanese page) 
(in English) mmWave communication / machine learning dataset / 5G / omnidirectional images / multipath propagation / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 69, SRW2024-5, pp. 22-27, June 2024.
Paper # SRW2024-5 
Date of Issue 2024-05-31 (SRW, MWPTHz) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
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)
Download PDF SRW2024-5 MWPTHz2024-5

Conference Information
Committee SRW MWPTHz  
Conference Date 2024-06-07 - 2024-06-07 
Place (in Japanese) (See Japanese page) 
Place (in English) Kikai-Shinko-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) WLAN， WPAN， Ad-hoc, mmWave, etc. 
Paper Information
Registration To SRW 
Conference Code 2024-06-SRW-MWPTHz 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Machine Learning Dataset Based on Omnidirectional Images for mmWave Multipath Propagation Prediction for Indoor-Short-Range Communication 
Sub Title (in English)  
Keyword(1) mmWave communication  
Keyword(2) machine learning dataset  
Keyword(3) 5G  
Keyword(4) omnidirectional images  
Keyword(5) multipath propagation  
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1st Author's Name Masaki Oda  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Yusuke Koda  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Norichika Ohmi  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Hiroshi Harada  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2024-06-07 11:10:00 
Presentation Time 25 minutes 
Registration for SRW 
Paper # SRW2024-5, MWPTHz2024-5 
Volume (vol) vol.124 
Number (no) no.69(SRW), no.70(MWPTHz) 
Page pp.22-27 
#Pages 6 
Date of Issue 2024-05-31 (SRW, MWPTHz) 

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