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| Presentation | 2019-10-18 13:00 A study on variety and size of input data for radio propagation prediction using a deep neural network Takahiro Hayashi, Tatsuya Nagao, Satoshi Ito (KDDI Research, Inc)  AP2019-102 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Not only has the volume of mobile traffic been increasing exponentially in recent years, making various services available, such as IoT and connected cars moving at high speed, has also become necessity; moreover, the quality of these services has to be extremely high. As a result, it is necessary to clarify the complicated characteristic of radio propagation. In this paper, we describe radio propagation prediction using a deep neural network (DNN) that can regress to non-linear functions without having to derive complex functions. DNN can learn the features needed for problem solving from input data, in other words, in radio propagation predictions, it is able to learn the environment parameters required for propagation prediction from spatial information that is input such as map data. Based on the evaluation results of propagation prediction with DNN using measurement data in urban area, we clarify the relationship between the variety and size of input data from the viewpoint of estimation accuracy and computational complexity. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Radio propagation prediction / Machine learning / Deep learning / Deep neural network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 228, AP2019-102, pp. 119-124, Oct. 2019. | |
| Paper # | AP2019-102 | |
| Date of Issue | 2019-10-10 (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) | |
| Download PDF | AP2019-102 |
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| Conference Information | |
| Committee | AP |
| Conference Date | 2019-10-17 - 2019-10-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Osaka Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Student Session, Antennas and Propagation |
| Paper Information | |
| Registration To | AP |
| Conference Code | 2019-10-AP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A study on variety and size of input data for radio propagation prediction using a deep neural network |
| Sub Title (in English) | |
| Keyword(1) | Radio propagation prediction |
| Keyword(2) | Machine learning |
| Keyword(3) | Deep learning |
| Keyword(4) | Deep neural network |
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| 1st Author's Name | Takahiro Hayashi |
| 1st Author's Affiliation | KDDI Research, Inc (KDDI Research, Inc) |
| 2nd Author's Name | Tatsuya Nagao |
| 2nd Author's Affiliation | KDDI Research, Inc (KDDI Research, Inc) |
| 3rd Author's Name | Satoshi Ito |
| 3rd Author's Affiliation | KDDI Research, Inc (KDDI Research, Inc) |
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| Speaker | Author-1 |
| Date Time | 2019-10-18 13:00:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2019-102 |
| Volume (vol) | vol.119 |
| Number (no) | no.228 |
| Page | pp.119-124 |
| #Pages | 6 |
| Date of Issue | 2019-10-10 (AP) |
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