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Paper Abstract and Keywords
Presentation 2025-07-31 16:30
Experimental Evaluation of Downlink Throughput Prediction Technology Utilizing Base Station Data in Public Cellular Systems
Keisuke Wakao, Motoharu Sasaki, Minoru Inomata, Genki Tajima, Mitsuki Nakamura, Kenichi Kawamura (NTT) RCS2025-88
Abstract (in Japanese) (See Japanese page) 
(in English) In 6G networks, handover and cooperative control of user equipment (UE) will become increasingly important to meet diverse quality-of-service (QoS) requirements in complex cell deployments. In this context, throughput prediction is gaining importance as a key decision-making indicator. Conventional throughput prediction methods have primarily relied on UE location information and signal quality metrics measurable by commercial devices, such as received power. However, these approaches often neglect the usage patterns of other devices in the area and the level of network congestion. To address
this, we propose a downlink throughput prediction method based on LightGBM (Light Gradient Boosting Machine), which leverages not only UE-side data but also base station (BS) data. We conducted measurements in a public LTE system by connecting UEs and applying full-buffer downlink UDP traffic, simultaneously capturing both UE and BS parameters. The proposed model was evaluated using the collected data. Compared to models that utilize only UE data, the proposed method achieves an improvement in RMSE from 19.2 Mbps to 12.1 Mbps.
Keyword (in Japanese) (See Japanese page) 
(in English) Cellular / Base station data / Throughput prediction / Experimental evaluation / Machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 133, RCS2025-88, pp. 68-73, July 2025.
Paper # RCS2025-88 
Date of Issue 2025-07-23 (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 RCS2025-88

Conference Information
Committee SR RCS RCC SeMI NS RISING HCL  
Conference Date 2025-07-30 - 2025-08-01 
Place (in Japanese) (See Japanese page) 
Place (in English) MALIOS 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To RCS 
Conference Code 2025-07-SR-RCS-RCC-SeMI-NS-RISING-HCL 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Experimental Evaluation of Downlink Throughput Prediction Technology Utilizing Base Station Data in Public Cellular Systems 
Sub Title (in English)  
Keyword(1) Cellular  
Keyword(2) Base station data  
Keyword(3) Throughput prediction  
Keyword(4) Experimental evaluation  
Keyword(5) Machine learning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Keisuke Wakao  
1st Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
2nd Author's Name Motoharu Sasaki  
2nd Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
3rd Author's Name Minoru Inomata  
3rd Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
4th Author's Name Genki Tajima  
4th Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
5th Author's Name Mitsuki Nakamura  
5th Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
6th Author's Name Kenichi Kawamura  
6th Author's Affiliation NIPPON TELEGRAPH AND TELEPHONE CORPORATION (NTT)
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Speaker Author-1 
Date Time 2025-07-31 16:30:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2025-88 
Volume (vol) vol.125 
Number (no) no.133 
Page pp.68-73 
#Pages 6 
Date of Issue 2025-07-23 (RCS) 

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