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| Presentation | 2024-01-19 15:25 [Invited Lecture] Radio Propagation Modeling Using CNN with Rectangular Map Data Tatsuya Nagao, Takahiro Hayashi (KDDI Research, Inc.)  AP2023-184 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, machine learning-based methods using environmental information as features have been proposed to model site-specific radio propagation characteristics. In particular, the convolutional neural network (CNN) -based techniques have been considered for extracting map data around the Tx/Rx points and using it as input data. CNN requires a fixed-size data input due to its structure, and there are concerns about accuracy degradation due to duplication or missing environmental information depending on the distance between the Tx and Rx and the map data extraction method. To address this issue, the author proposed a modeling method that uses the recurrent neural network (RNN) to allow input of variable-size map data and showed improved accuracy. However, RNN is a method of sequentially inputting series data and updating feature vectors, which increases the time required for training and prediction. In addition, when input to an RNN, map data must be converted to vector data, resulting in the loss of spatial information. In this paper, we propose CNN-based modeling using rectangular environment map data. Finally, we clarify the effectiveness of the proposed method by evaluation using measurement data in an urban area. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Radio Propagation Prediction / Machine Learning / Convolutional Nerural Network / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 336, AP2023-184, pp. 126-130, Jan. 2024. | |
| Paper # | AP2023-184 | |
| Date of Issue | 2024-01-10 (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 | AP2023-184 |
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| Conference Information | |
| Committee | AP WPT |
| Conference Date | 2024-01-17 - 2024-01-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tokimate, Niigata University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Radio propagation, Wireless transmission technology, Antennas and Propagation |
| Paper Information | |
| Registration To | AP |
| Conference Code | 2024-01-AP-WPT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Radio Propagation Modeling Using CNN with Rectangular Map Data |
| Sub Title (in English) | |
| Keyword(1) | Radio Propagation Prediction |
| Keyword(2) | Machine Learning |
| Keyword(3) | Convolutional Nerural Network |
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| 1st Author's Name | Tatsuya Nagao |
| 1st Author's Affiliation | KDDI Research, Inc. (KDDI Research, Inc.) |
| 2nd Author's Name | Takahiro Hayashi |
| 2nd Author's Affiliation | KDDI Research, Inc. (KDDI Research, Inc.) |
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| Speaker | Author-1 |
| Date Time | 2024-01-19 15:25:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2023-184 |
| Volume (vol) | vol.123 |
| Number (no) | no.336 |
| Page | pp.126-130 |
| #Pages | 5 |
| Date of Issue | 2024-01-10 (AP) |
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