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 |
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 |
AP2023-212 |
|