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| Paper Abstract and Keywords | ||
| Presentation | 2022-03-09 15:55 Model selection for link quality prediction based on physical space information Hisashi Nagata, Riichi Kudo, Kahoko Takahashi, Tomoaki Ogawa (NTT)  CQ2021-106 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | With the development of wireless communication technology, it is expected that all things will be connected to the network and various applications will be created. Diversifying applications are expected to make the requirements for wireless communication more complicated and sophisticated. For example, considering the management, monitoring, and operation of autonomously operating mobile robots, it is necessary to predict wireless communication quality with high accuracy in order to ensure highly reliable wireless communication quality. Predicting the quality of wireless links is important to ensure highly reliable wireless communication. To predict the quality of wireless communication, generate a prediction model by deep learning using physical spatial information obtained from cameras and the like. A method has been proposed. In this paper, when camera information and terminal position information can be used as physical spatial information, high performance can be achieved by individually forming a prediction model and a reliability evaluation model, respectively. We propose a wireless communication quality prediction system that achieves both model expandability. The results of evaluating the prediction performance using indoor experimental data are shown. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | wireless LAN / machine learning / deep learning / communication quality prediction / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 421, CQ2021-106, pp. 31-36, March 2022. | |
| Paper # | CQ2021-106 | |
| Date of Issue | 2022-03-02 (CQ) | |
| 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 | CQ2021-106 |
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| Conference Information | |
| Committee | CQ IMQ MVE IE |
| Conference Date | 2022-03-09 - 2022-03-11 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online (Zoom) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Media of five senses, Multimedia, Media experience, Picture codinge, Image media quality, Network,quality and reliability, etc |
| Paper Information | |
| Registration To | CQ |
| Conference Code | 2022-03-CQ-IMQ-MVE-IE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Model selection for link quality prediction based on physical space information |
| Sub Title (in English) | |
| Keyword(1) | wireless LAN |
| Keyword(2) | machine learning |
| Keyword(3) | deep learning |
| Keyword(4) | communication quality prediction |
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| 1st Author's Name | Hisashi Nagata |
| 1st Author's Affiliation | NTT Network Innovation Laboratories (NTT) |
| 2nd Author's Name | Riichi Kudo |
| 2nd Author's Affiliation | NTT Network Innovation Laboratories (NTT) |
| 3rd Author's Name | Kahoko Takahashi |
| 3rd Author's Affiliation | NTT Network Innovation Laboratories (NTT) |
| 4th Author's Name | Tomoaki Ogawa |
| 4th Author's Affiliation | NTT Network Innovation Laboratories (NTT) |
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| Speaker | Author-1 |
| Date Time | 2022-03-09 15:55:00 |
| Presentation Time | 25 minutes |
| Registration for | CQ |
| Paper # | CQ2021-106 |
| Volume (vol) | vol.121 |
| Number (no) | no.421 |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2022-03-02 (CQ) |
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