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Paper Abstract and Keywords
Presentation 2021-01-20 16:05
[Invited Talk] A machine learning approach to data generation in networks
Kohei Watabe (Nagaoka Univ. of Tech.) CQ2020-71
Abstract (in Japanese) (See Japanese page) 
(in English) When we evaluate communication networks and protocols/applications running on them, it is important to demonstrate their performance through experiments and simulations with real data. By performing experiments/simulations based on various real data such as network topology, traffic, trajectories of mobile devices, and communication quality, it is possible to demonstrate the performance in the real environment. However, most of real data related to communication networks are used only within specific companies and organizations, and they are not open to the public. Then many researchers and developers cannot access these data. In addition, there is not always enough real data with desirable characteristics that suit a purpose of a simulation or an experiment. Traditionally, if real data are not available, we have no choice but to obtain experimental results with data generated based on a stochastic model. However, it does not always match the result with real data. In this paper, we introduce recent trends in related research and our recent works that enable realistic data generation. The generator developed by the authors enables tuning of any parameters while maintaining characteristics of real data, thereby enables flexible simulations and experiments.
Keyword (in Japanese) (See Japanese page) 
(in English) communication data generation / traffic / network topology / trajectory / machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 314, CQ2020-71, pp. 57-57, Jan. 2021.
Paper # CQ2020-71 
Date of Issue 2021-01-13 (CQ) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee CQ CBE  
Conference Date 2021-01-20 - 2021-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) AR/VR, Broadcasting Service, Video/Voice Services Quality, High Realistic, User Behavior/Psychology, User Experience, Media Quality, Network Quality and QoS Control, Networks and Communications at Disaster, User Behavior, Machine Learning, Video Communication, etc. 
Paper Information
Registration To CQ 
Conference Code 2021-01-CQ-CBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A machine learning approach to data generation in networks 
Sub Title (in English)  
Keyword(1) communication data generation  
Keyword(2) traffic  
Keyword(3) network topology  
Keyword(4) trajectory  
Keyword(5) machine learning  
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1st Author's Name Kohei Watabe  
1st Author's Affiliation Nagaoka University of Technology (Nagaoka Univ. of Tech.)
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Speaker
Date Time 2021-01-20 16:05:00 
Presentation Time 35 
Registration for CQ 
Paper # IEICE-CQ2020-71 
Volume (vol) IEICE-120 
Number (no) no.314 
Page p.57 
#Pages IEICE-1 
Date of Issue IEICE-CQ-2021-01-13 


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