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Paper Abstract and Keywords
Presentation 2022-09-14 14:40
A Study of Building Map Representation for Spatiotemporal Channel Parameters Estimation Model by Machine Learning
Keiji Yoshikawa, Tatsuya Nagao, Kazuki Takezawa, Takahiro Hayashi (KDDI Research, Inc) AP2022-78
Abstract (in Japanese) (See Japanese page) 
(in English) Wireless emulators are being developed to design and evaluate wireless systems in virtual space. To emulate various environments with high accuracy, a highly accurate propagation model is required for individual environments. In particular, the modeling of not only propagation loss but also spatiotemporal characteristics is necessary to verify fading variations due to multiple paths. Recently, machine learning methods using spherical images and building maps have been proposed as models for estimating site-specific propagation characteristics. However, information on buildings where reflections and diffractions on multiple paths occur is important for modeling spatiotemporal characteristics. These reflections and diffractions occur at various locations out of sight of the transmitter and receiver and occur in large numbers in the vicinity of the transmitter and receiver. Therefore, depending on the representation format of the buildings, there is a concern that accuracy may be degraded due to insufficient information, and the data size may be increased due to unnecessary information. This paper proposes an input format that represents buildings based on polar coordinates centered on the transmitter/receiver for estimation that takes buildings into account. The effectiveness of the proposed method is verified through a simulation evaluation in which the delay spread is calculated as a spatiotemporal parameter.
Keyword (in Japanese) (See Japanese page) 
(in English) radio propagation prediction / spatiotemporal parameters / machine learning / building map / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 182, AP2022-78, pp. 38-43, Sept. 2022.
Paper # AP2022-78 
Date of Issue 2022-09-07 (AP) 
ISSN Online edition: ISSN 2432-6380
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 AP2022-78

Conference Information
Committee AP MW  
Conference Date 2022-09-14 - 2022-09-16 
Place (in Japanese) (See Japanese page) 
Place (in English) The Museum of Art, EHIME 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Microwave, Millimeter wave 
Paper Information
Registration To AP 
Conference Code 2022-09-AP-MW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Building Map Representation for Spatiotemporal Channel Parameters Estimation Model by Machine Learning 
Sub Title (in English)  
Keyword(1) radio propagation prediction  
Keyword(2) spatiotemporal parameters  
Keyword(3) machine learning  
Keyword(4) building map  
1st Author's Name Keiji Yoshikawa  
1st Author's Affiliation KDDI Research, Inc (KDDI Research, Inc)
2nd Author's Name Tatsuya Nagao  
2nd Author's Affiliation KDDI Research, Inc (KDDI Research, Inc)
3rd Author's Name Kazuki Takezawa  
3rd Author's Affiliation KDDI Research, Inc (KDDI Research, Inc)
4th Author's Name Takahiro Hayashi  
4th Author's Affiliation KDDI Research, Inc (KDDI Research, Inc)
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Speaker Author-1 
Date Time 2022-09-14 14:40:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2022-78 
Volume (vol) vol.122 
Number (no) no.182 
Page pp.38-43 
Date of Issue 2022-09-07 (AP) 

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