Paper Abstract and Keywords |
Presentation |
2022-10-20 14:00
A Study on Rain Attenuation Prediction Method by Deep Learning 2
-- Error Characteristics -- Yuji Komatsuya, Tetsuro Imai (TDU), Miyuki Hirose (Kyutech) AP2022-141 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, the frequency used in wireless systems has got higher significantly, such as B5G (6G), HAPS, etc., and the importance of predicting rainfall attenuation has increased. We proposed a rain attenuation prediction method by deep learning which inputs rainfall rate and path distance, and conducted prediction. In this study, we improved the input data and scale of the proposed model, achieved broad-sense performance improvement of the proposed model. Furthermore, we indicated superiority of proposed model over conventional method using rain attenuation coefficients defined in the ITU-R Recommendation. In addition, we considered the output result of proposed model. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Rain Attenuation / Deep Learningp / CNN / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 122, no. 214, AP2022-141, pp. 211-215, Oct. 2022. |
Paper # |
AP2022-141 |
Date of Issue |
2022-10-12 (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) |
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AP2022-141 |
Conference Information |
Committee |
AP |
Conference Date |
2022-10-19 - 2022-10-20 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
GIFU CITY CULTURE CENTER |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Student Session, Antennas and Propagation |
Paper Information |
Registration To |
AP |
Conference Code |
2022-10-AP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Rain Attenuation Prediction Method by Deep Learning 2 |
Sub Title (in English) |
Error Characteristics |
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Rain Attenuation |
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Deep Learningp |
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CNN |
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1st Author's Name |
Yuji Komatsuya |
1st Author's Affiliation |
Tokyo Denki University (TDU) |
2nd Author's Name |
Tetsuro Imai |
2nd Author's Affiliation |
Tokyo Denki University (TDU) |
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Miyuki Hirose |
3rd Author's Affiliation |
Kyushu Institute of Technology (Kyutech) |
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Speaker |
Author-1 |
Date Time |
2022-10-20 14:00:00 |
Presentation Time |
25 minutes |
Registration for |
AP |
Paper # |
AP2022-141 |
Volume (vol) |
vol.122 |
Number (no) |
no.214 |
Page |
pp.211-215 |
#Pages |
5 |
Date of Issue |
2022-10-12 (AP) |
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