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Paper Abstract and Keywords
Presentation 2022-02-24 10:05
Study on Bandwidth usage Reduction by Traffic Prediction Using Transfer Learning in Satellite Communication Systems
Kazumasab Tamada, Yuichi Kawamoto, Nei Kato (Tohoku Univ.) SAT2021-54
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, Internet traffic has been increasing owing to the widespread use of teleworking and flat-rate video distribution services. Such enormous traffic is also expected to transmit over the satellite communications lines of the Beyond 5G backhaul network and the fluctuation patterns will become more diversified. The resources of satellite communication are scarcer than terrestrial communication. Therefore, this research focuses on efficient satellite network resource operation. Traffic forecasting is one of the promising approaches for efficient resource allocation. There are several methods for traffic prediction of terrestrial communication using machine learning. However, there are no literatures focusing on traffic prediction using ML over satellite communication. This is challenging due to high bandwidth consumption by the existing prediction methods over limited satellite bandwidth that can lower bandwidth usage for users. Therefore, this paper proposes a traffic prediction method that uses transfer learning to reduce the bandwidth usage by the proposed approach. Furthermore, we perform simulations to compare the prediction accuracy of the proposed method with the conventional approach to verify the performance of the proposed approach.
Keyword (in Japanese) (See Japanese page) 
(in English) Satellite communication / Machine learning / Traffic prediction / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 379, SAT2021-54, pp. 7-12, Feb. 2022.
Paper # SAT2021-54 
Date of Issue 2022-02-17 (SAT) 
ISSN 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)
Download PDF SAT2021-54

Conference Information
Committee SAT SANE  
Conference Date 2022-02-24 - 2022-02-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Satellite technology, etc. 
Paper Information
Registration To SAT 
Conference Code 2022-02-SAT-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Bandwidth usage Reduction by Traffic Prediction Using Transfer Learning in Satellite Communication Systems 
Sub Title (in English)  
Keyword(1) Satellite communication  
Keyword(2) Machine learning  
Keyword(3) Traffic prediction  
1st Author's Name Kazumasab Tamada  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Yuichi Kawamoto  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Nei Kato  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2022-02-24 10:05:00 
Presentation Time 25 minutes 
Registration for SAT 
Paper # SAT2021-54 
Volume (vol) vol.121 
Number (no) no.379 
Page pp.7-12 
Date of Issue 2022-02-17 (SAT) 

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