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Paper Abstract and Keywords
Presentation 2021-07-29 12:40
[Invited Lecture] Radio Propagation Prediction using Geographical Classification by Clustering and Convolutional Neural Network
Tatsuya Nagao, Takahiro Hayashi (KDDI Research) AP2021-36
Abstract (in Japanese) (See Japanese page) 
(in English) Beyond 5G is expected to play an essential role in the future social infrastructure, and there is a need for a new system that can emulate the behavior of wireless communication in real environments with various scenarios in virtual space. To realize this radio emulation system, it is essential to have the technique to model the radio propagation characteristics in various propagation environments with high accuracy. For this purpose, it is necessary to verify the prediction accuracy and the appropriate range of application of the prediction model in a wide range of geographical features, including urban, suburban, and rural areas. In this paper, we propose a method to classify the geographical features by clustering map data, and to model the site-specific path loss characteristics corresponding to the geographical features using Convolutional Neural Networks. The evaluation results using measurement data confirmed that the prediction accuracy was improved by modeling for each cluster.
Keyword (in Japanese) (See Japanese page) 
(in English) Radio propagation prediction / Machine Learning / Clustering / Deep Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 126, AP2021-36, pp. 70-75, July 2021.
Paper # AP2021-36 
Date of Issue 2021-07-21 (AP) 
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 AP2021-36

Conference Information
Committee AP SANE SAT  
Conference Date 2021-07-28 - 2021-07-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Remote sensing, Sattelite Communication, Radio propagation, Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2021-07-AP-SANE-SAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Radio Propagation Prediction using Geographical Classification by Clustering and Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Radio propagation prediction  
Keyword(2) Machine Learning  
Keyword(3) Clustering  
Keyword(4) Deep Learning  
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1st Author's Name Tatsuya Nagao  
1st Author's Affiliation KDDI Research, Inc. (KDDI Research)
2nd Author's Name Takahiro Hayashi  
2nd Author's Affiliation KDDI Research, Inc. (KDDI Research)
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Speaker Author-1 
Date Time 2021-07-29 12:40:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2021-36 
Volume (vol) vol.121 
Number (no) no.126 
Page pp.70-75 
#Pages 6 
Date of Issue 2021-07-21 (AP) 

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