Paper Abstract and Keywords |
Presentation |
2016-03-03 09:00
Throughput estimation using online machine learning algorithm from depth-images for mmWave communications Hironao Okamoto, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) SR2015-96 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In mmWave communications, received signal strength sharply decreases when pedestrians block line-of-sight (LOS) paths. To solve the human blockage problem, a base station handover has been assessed. In order to quickly transfer a mobile station communicating with one base station to another base station which provides higher throughput, the handover system obtains and updates throughput information between the station and each of base stations. Therefore, many control signals are required to transmit to obtain the throughput information and would waste mmWave radio resources. In this paper, we propose a mmWave throughput estimation scheme using an online machine learning algorithm and depth-image. A system with the scheme learns the relationships between depth-images and throughputs, and estimates throughputs from depth-images. The system enables optimized base station handover without control signals. The algorithm enables the handover system to estimate throughput fast and adaptively. We implemented a testbed using IEEE 802.11ad mmWave wireless LAN devices and an RGB-D camera. The experiment results confirm that the scheme estimates temporal variation of throughputs more accurately than a compared scheme in various scenarios. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
mmWave / machine learning / RGB-D camera / communication quality estimation / IEEE 802.11ad / / / |
Reference Info. |
IEICE Tech. Rep., vol. 115, no. 473, SR2015-96, pp. 47-52, March 2016. |
Paper # |
SR2015-96 |
Date of Issue |
2016-02-24 (SR) |
ISSN |
Print edition: ISSN 0913-5685 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) |
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SR2015-96 |
Conference Information |
Committee |
RCS CCS SR SRW |
Conference Date |
2016-03-02 - 2016-03-04 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Tokyo Institute of Technology |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Mobile Communication Workshop |
Paper Information |
Registration To |
SR |
Conference Code |
2016-03-RCS-CCS-SR-SRW |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Throughput estimation using online machine learning algorithm from depth-images for mmWave communications |
Sub Title (in English) |
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mmWave |
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machine learning |
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RGB-D camera |
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communication quality estimation |
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IEEE 802.11ad |
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1st Author's Name |
Hironao Okamoto |
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 |
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Kyoto University (Kyoto Univ.) |
4th Author's Name |
Koji Yamamoto |
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Kyoto University (Kyoto Univ.) |
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Speaker |
Author-1 |
Date Time |
2016-03-03 09:00:00 |
Presentation Time |
25 minutes |
Registration for |
SR |
Paper # |
SR2015-96 |
Volume (vol) |
vol.115 |
Number (no) |
no.473 |
Page |
pp.47-52 |
#Pages |
6 |
Date of Issue |
2016-02-24 (SR) |
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