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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
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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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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)  
Keyword(1) mmWave  
Keyword(2) machine learning  
Keyword(3) RGB-D camera  
Keyword(4) communication quality estimation  
Keyword(5) 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  
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 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
Date of Issue 2016-02-24 (SR) 


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