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Paper Abstract and Keywords
Presentation 2019-08-23 11:45
Wireless Network Control Enabled by Data Assessment Using Machine Learning
Ryoichi Shinkuma, Takayuki Nishio (Kyoto Univ.) RCS2019-166
Abstract (in Japanese) (See Japanese page) 
(in English) The real-time prediction of spatial information is promising for next-generation mobile networks. Recent developments in machine learning technology have enabled prediction of spatial information, which will be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Other key enablers for forming spatial information are image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. This talk presents a framework of data assessment and prioritization that reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In the framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. A numerical evaluation using an actual vehicle mobility dataset demonstrated the validity of the framework.
Keyword (in Japanese) (See Japanese page) 
(in English) machine learning / data importance / spatial information / real-time prediction / wireless network control / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 176, RCS2019-166, pp. 109-112, Aug. 2019.
Paper # RCS2019-166 
Date of Issue 2019-08-15 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 SAT  
Conference Date 2019-08-22 - 2019-08-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagoya University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Satellite Communications, Broadcasting, Forward Error Correction, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2019-08-RCS-SAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Wireless Network Control Enabled by Data Assessment Using Machine Learning 
Sub Title (in English)  
Keyword(1) machine learning  
Keyword(2) data importance  
Keyword(3) spatial information  
Keyword(4) real-time prediction  
Keyword(5) wireless network control  
1st Author's Name Ryoichi Shinkuma  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Takayuki Nishio  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
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Date Time 2019-08-23 11:45:00 
Presentation Time 20 
Registration for RCS 
Paper # IEICE-RCS2019-166 
Volume (vol) IEICE-119 
Number (no) no.176 
Page pp.109-112 
#Pages IEICE-4 
Date of Issue IEICE-RCS-2019-08-15 

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