Paper Abstract and Keywords |
Presentation |
2020-12-17 13:50
A Study on Urban Structure Map Extraction for Radio Propagation Prediction using XGBoost Tatsuya Nagao, Takahiro Hayashi (KDDI Research) AP2020-98 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recently, the rapid increase in mobile data traffic and the diversification of wireless communication services have led to demand for the development of high-quality mobile service areas. Therefore, the modeling of complex radio propagation characteristics in the practical communication environment is an important issue. Radio propagation prediction methods based on machine learning have been proposed, but no study on the appropriate range of urban structure maps as features has been conducted. In this paper, we clarify the appropriate extraction range of map data, applying XGBoost to the machine learning algorithm. Moreover, since XGBoost can output the importance of the input features, we can extract only those features that are useful for training and input them to machine learning. This approach is expected to improve both prediction accuracy and speed. The evaluation using measurement data obtained in urban areas showed that the computation time for learning can be reduced while improving the prediction error. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Radio propagation prediction / Machine Learning / XGBoost / Feature selection / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 299, AP2020-98, pp. 13-17, Dec. 2020. |
Paper # |
AP2020-98 |
Date of Issue |
2020-12-10 (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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AP2020-98 |
Conference Information |
Committee |
AP |
Conference Date |
2020-12-17 - 2020-12-17 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Antennas and Propagation |
Paper Information |
Registration To |
AP |
Conference Code |
2020-12-AP |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
A Study on Urban Structure Map Extraction for Radio Propagation Prediction using XGBoost |
Sub Title (in English) |
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Radio propagation prediction |
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Machine Learning |
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XGBoost |
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Feature selection |
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1st Author's Name |
Tatsuya Nagao |
1st Author's Affiliation |
KDDI Research, Inc. (KDDI Research) |
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Takahiro Hayashi |
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KDDI Research, Inc. (KDDI Research) |
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Speaker |
Author-1 |
Date Time |
2020-12-17 13:50:00 |
Presentation Time |
25 minutes |
Registration for |
AP |
Paper # |
AP2020-98 |
Volume (vol) |
vol.120 |
Number (no) |
no.299 |
Page |
pp.13-17 |
#Pages |
5 |
Date of Issue |
2020-12-10 (AP) |
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