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Paper Abstract and Keywords
Presentation 2026-03-06 16:05
Throughput Prediction for Public Cellular Networks at Future Driving Positions Using Physical Layer Parameters
Motoharu Sasaki, Mitsuki Nakamura, Keisuke Wakao, Genki Tajima, Kenichi Kawamura (NTT) RCS2025-298
Abstract (in Japanese) (See Japanese page) 
(in English) When predicting the quality of wireless communication over public cellular networks, detailed parameters that have a significant impact on throughput can be obtained using dedicated terminals and software, but installing such parameters in all operational vehicles is cost-prohibitive. In this study, we propose a method for predicting the throughput of public cellular networks using physical layer parameters that can be obtained using commercial off-the-shelf (COTS) terminals. We explain our method for estimating highly position-dependent physical layer parameters, such as received power at the vehicle’s driving position a few seconds later, using spatial interpolation based on training data of the vehicle’s driving route. We then use decision-tree-based machine learning to predict throughput from the estimated physical layer parameters, and present the results of an accuracy evaluation using actual measurement data.
Keyword (in Japanese) (See Japanese page) 
(in English) Autonomous vehicle / predictive QoS / machne-learning / spatial interpolation / LightGBM / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 387, RCS2025-298, pp. 227-232, March 2026.
Paper # RCS2025-298 
Date of Issue 2026-02-25 (RCS) 
ISSN 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)
Download PDF RCS2025-298

Conference Information
Committee RCS SR SRW  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo University of Science 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2026-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Throughput Prediction for Public Cellular Networks at Future Driving Positions Using Physical Layer Parameters 
Sub Title (in English)  
Keyword(1) Autonomous vehicle  
Keyword(2) predictive QoS  
Keyword(3) machne-learning  
Keyword(4) spatial interpolation  
Keyword(5) LightGBM  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Motoharu Sasaki  
1st Author's Affiliation NTT, Inc. (NTT)
2nd Author's Name Mitsuki Nakamura  
2nd Author's Affiliation NTT, Inc. (NTT)
3rd Author's Name Keisuke Wakao  
3rd Author's Affiliation NTT, Inc. (NTT)
4th Author's Name Genki Tajima  
4th Author's Affiliation NTT, Inc. (NTT)
5th Author's Name Kenichi Kawamura  
5th Author's Affiliation NTT, Inc. (NTT)
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Speaker Author-1 
Date Time 2026-03-06 16:05:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2025-298 
Volume (vol) vol.125 
Number (no) no.387 
Page pp.227-232 
#Pages 6 
Date of Issue 2026-02-25 (RCS) 

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