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| Presentation | 2022-01-20 15:10 [Invited Talk] Wireless link quality prediction using physical space information in Society 5.0 Riichi Kudo, Kahoko Takahashi, Hisashi Nagata, Tomoki Murakami, Tomoaki Ogawa (NTT)  IT2021-44 SIP2021-52 RCS2021-212 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Society 5.0 / Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. / Bounding box / link quality prediction / machine learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 329, RCS2021-212, pp. 93-94, Jan. 2022. | |
| Paper # | RCS2021-212 | |
| Date of Issue | 2022-01-13 (IT, SIP, RCS) | |
| 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 | IT2021-44 SIP2021-52 RCS2021-212 |
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| Conference Information | |
| Committee | RCS SIP IT |
| Conference Date | 2022-01-20 - 2022-01-21 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | RCS |
| Conference Code | 2022-01-RCS-SIP-IT |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Wireless link quality prediction using physical space information in Society 5.0 |
| Sub Title (in English) | |
| Keyword(1) | Society 5.0 |
| Keyword(2) | Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It is expected that various novel services emerge in Society 5.0 that is based on Internet of Things (IoT) by a high degree of convergence between cyberspace (virtual world) and physical space (real world). This report discusses the potential of the physical space information use for the future wireless communication systems in Society 5.0. In wireless LAN systems, the throughput prediction was conducted using physical space information such as robot position information and camera images. We generated the prediction models using deep learning algorithms including recurrent neural network (RNN) and the indoor experiments were conducted for the evaluation. The results showed that the physical space information enabled the long term prediction. |
| Keyword(3) | Bounding box |
| Keyword(4) | link quality prediction |
| Keyword(5) | machine learning |
| Keyword(6) | |
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| Keyword(8) | |
| 1st Author's Name | Riichi Kudo |
| 1st Author's Affiliation | NTT (NTT) |
| 2nd Author's Name | Kahoko Takahashi |
| 2nd Author's Affiliation | NTT (NTT) |
| 3rd Author's Name | Hisashi Nagata |
| 3rd Author's Affiliation | NTT (NTT) |
| 4th Author's Name | Tomoki Murakami |
| 4th Author's Affiliation | NTT (NTT) |
| 5th Author's Name | Tomoaki Ogawa |
| 5th Author's Affiliation | NTT (NTT) |
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| Speaker | Author-1 |
| Date Time | 2022-01-20 15:10:00 |
| Presentation Time | 50 minutes |
| Registration for | RCS |
| Paper # | IT2021-44, SIP2021-52, RCS2021-212 |
| Volume (vol) | vol.121 |
| Number (no) | no.327(IT), no.328(SIP), no.329(RCS) |
| Page | pp.93-94 |
| #Pages | 2 |
| Date of Issue | 2022-01-13 (IT, SIP, RCS) |
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