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| Presentation | 2023-05-12 10:00 A Study on Radio Propagation Modeling using RNN-Encoder with Variable-Size Map Data Tatsuya Nagao, Takahiro Hayashi (KDDI Research)  AP2023-17 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | For efficient evaluation of the performance of wireless systems in physical space, wireless emulation techniques in virtual space are increasingly expected. To realize accurate emulations, it is essential to model radio propagation characteristics, especially path loss is one of the most fundamental characteristics. Recently, machine learning-based methods for site-specific path loss modeling have been proposed. Most apply the convolutional neural network (CNN) with map data around Tx and Rx. However, CNN needs to input the fixed-size data, which might cause accuracy degradation due to lacking or redundancy of information. Therefore, this paper proposes the path loss modeling method based on the recurrent neural network (RNN), handling map data as sequential 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 / Recurrent Neural Network / Gated Recurrent Unit / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 16, AP2023-17, pp. 48-53, May 2023. | |
| Paper # | AP2023-17 | |
| Date of Issue | 2023-05-04 (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 | AP2023-17 |
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| Conference Information | |
| Committee | AP |
| Conference Date | 2023-05-11 - 2023-05-12 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Gender Equality Center |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Antennas and Propagation |
| Paper Information | |
| Registration To | AP |
| Conference Code | 2023-05-AP |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study on Radio Propagation Modeling using RNN-Encoder with Variable-Size Map Data |
| Sub Title (in English) | |
| Keyword(1) | Radio Propagation Prediction |
| Keyword(2) | Machine Learning |
| Keyword(3) | Recurrent Neural Network |
| Keyword(4) | Gated Recurrent Unit |
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| 1st Author's Name | Tatsuya Nagao |
| 1st Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
| 2nd Author's Name | Takahiro Hayashi |
| 2nd Author's Affiliation | KDDI Research, Inc. (KDDI Research) |
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| Speaker | Author-1 |
| Date Time | 2023-05-12 10:00:00 |
| Presentation Time | 25 minutes |
| Registration for | AP |
| Paper # | AP2023-17 |
| Volume (vol) | vol.123 |
| Number (no) | no.16 |
| Page | pp.48-53 |
| #Pages | 6 |
| Date of Issue | 2023-05-04 (AP) |
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