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| Presentation | 2019-11-06 13:25 [Poster Presentation] A Study of Received Power Prediction Using Ray-tracing Simulation and Deep Learning for mmWave Communications Masahiro Iwasaki, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ)  SRW2019-34 SeMI2019-78 CNR2019-28 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Machine learning based received power prediction has been studied. These methods learn Features such as surrounding map information and received power. However, these methods require a large number of training datasets for preparing the accurate prediction model due to using a deep learning. In order to reduce cost for the dataset preparation, this paper proposes a pre-training method using radio propagation simulation. In this method, a prediction model trained with simulation data is transfered and fine-tuned using a small amount of dataset obtained in real environment to provide accurate prediction in the environment. The performance of the proposed method was evaluated by using the radio propagation simulation results. The evaluations show that the proposed method reduces the amount of training datasets and the computational time to achieve certain prediction accuracy. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deep learning / Radio propagation simulation / mmWave communication / Received power prediction / Transfer learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 266, SeMI2019-78, pp. 73-74, Nov. 2019. | |
| Paper # | SeMI2019-78 | |
| Date of Issue | 2019-10-29 (SRW, SeMI, CNR) | |
| 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 | SRW2019-34 SeMI2019-78 CNR2019-28 |
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| Conference Information | |
| Committee | SRW SeMI CNR |
| Conference Date | 2019-11-05 - 2019-11-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Kozo Keisaku Engineering Inc. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | IoT Workshop (Oral or Poster presentation) |
| Paper Information | |
| Registration To | SeMI |
| Conference Code | 2019-11-SRW-SeMI-CNR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Received Power Prediction Using Ray-tracing Simulation and Deep Learning for mmWave Communications |
| Sub Title (in English) | |
| Keyword(1) | Deep learning |
| Keyword(2) | Radio propagation simulation |
| Keyword(3) | mmWave communication |
| Keyword(4) | Received power prediction |
| Keyword(5) | Transfer learning |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Masahiro Iwasaki |
| 1st Author's Affiliation | Kyoto University (Kyoto Univ) |
| 2nd Author's Name | Takayuki Nishio |
| 2nd Author's Affiliation | Kyoto University (Kyoto Univ) |
| 3rd Author's Name | Masahiro Morikura |
| 3rd Author's Affiliation | Kyoto University (Kyoto Univ) |
| 4th Author's Name | Koji Yamamoto |
| 4th Author's Affiliation | Kyoto University (Kyoto Univ) |
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| Speaker | Author-1 |
| Date Time | 2019-11-06 13:25:00 |
| Presentation Time | 60 minutes |
| Registration for | SeMI |
| Paper # | SRW2019-34, SeMI2019-78, CNR2019-28 |
| Volume (vol) | vol.119 |
| Number (no) | no.265(SRW), no.266(SeMI), no.267(CNR) |
| Page | pp.53-54(SRW), pp.73-74(SeMI), pp.51-52(CNR) |
| #Pages | 2 |
| Date of Issue | 2019-10-29 (SRW, SeMI, CNR) |
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