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Paper Abstract and Keywords
Presentation 2024-03-15 10:25
A Study on Path loss characteristics estimation methods considering geographical conditions for designing narrowband DR-IoT communication system
Takato Ikegame, Naoki Ikeda, Motonari Imai, Tetsushi Ikegami (Meiji Univ.), Mineo Takai (Osaka Univ.), Susumu Ishihara (Shizuoka Univ.), Arata Kato, Shugo Kajita (STE) AP2023-212
Abstract (in Japanese) (See Japanese page) 
(in English) A versatile variable-range IoT communication system using the VHF-High band, Diversified-Range IoT (DR-IoT) is being considered as an enabling technology for digital self-managed radio networks to enable multiple disaster response organizations to efficiently collect, share, analyze information and make decisions when the existing communication infrastructure is not available due to a large-scale disaster. In DR-IoT system, it is assumed that interference can be suppressed if the output power is appropriately adjusted according to the geographic location and topology of the radios, which requires estimation and understanding of the path loss characteristics of the VHF-High band to be used. This paper discusses path loss characteristics estimation methods using a convolutional neural network (CNN) for designing stations using geographic placement of radios, based on outdoor measured data from field experiments. The propagation model ITM (Longley-Rice), which is considered effective for understanding path loss characteristics in mountainous terrain, and spatial information are used as input map data for the CNN to learn. The input map data enables the estimation of path loss characteristics considering complex geographical conditions.
Keyword (in Japanese) (See Japanese page) 
(in English) DR-IoT / Disaster response / ITM (Longley-Rice) / Deep learning / Convolutional Neural Network (CNN) / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 441, AP2023-212, pp. 63-67, March 2024.
Paper # AP2023-212 
Date of Issue 2024-03-07 (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 AP2023-212

Conference Information
Committee AP  
Conference Date 2024-03-14 - 2024-03-15 
Place (in Japanese) (See Japanese page) 
Place (in English) UNIVERSITY OF FUKUI 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2024-03-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Path loss characteristics estimation methods considering geographical conditions for designing narrowband DR-IoT communication system 
Sub Title (in English)  
Keyword(1) DR-IoT  
Keyword(2) Disaster response  
Keyword(3) ITM (Longley-Rice)  
Keyword(4) Deep learning  
Keyword(5) Convolutional Neural Network (CNN)  
1st Author's Name Takato Ikegame  
1st Author's Affiliation Meiji University (Meiji Univ.)
2nd Author's Name Naoki Ikeda  
2nd Author's Affiliation Meiji University (Meiji Univ.)
3rd Author's Name Motonari Imai  
3rd Author's Affiliation Meiji University (Meiji Univ.)
4th Author's Name Tetsushi Ikegami  
4th Author's Affiliation Meiji University (Meiji Univ.)
5th Author's Name Mineo Takai  
5th Author's Affiliation Osaka University (Osaka Univ.)
6th Author's Name Susumu Ishihara  
6th Author's Affiliation Shizuoka University (Shizuoka Univ.)
7th Author's Name Arata Kato  
7th Author's Affiliation Space-Time Engineering Japan, Inc (STE)
8th Author's Name Shugo Kajita  
8th Author's Affiliation Space-Time Engineering Japan, Inc (STE)
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Speaker Author-1 
Date Time 2024-03-15 10:25:00 
Presentation Time 25 minutes 
Registration for AP 
Paper # AP2023-212 
Volume (vol) vol.123 
Number (no) no.441 
Page pp.63-67 
Date of Issue 2024-03-07 (AP) 

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