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Paper Abstract and Keywords
Presentation 2026-02-19 09:10
A Multi-Agent Reinforcement Learning Approach for Wide-Area Optimization of Frequency Resource Allocation in Satellite-Ground Links
Nao Kubota, Hiroaki Hashida, Yuichi Kawamoto, Nei Kato (Tohoku Univ.), Yohei Hasegawa, Masayuki Ariyoshi (NEC) SAT2025-64
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, integrated satellite–terrestrial networks using low Earth orbit (LEO) satellite constellations have attracted attention as a solution to growing communication demand. This study considers the downlink from LEO satellites to ground stations under rainfall attenuation, where weather variations, traffic fluctuations, and handover data can lead to inefficient frequency resource allocation and increased communication delay. To reduce system-wide delay, we apply multi-agent reinforcement learning to dynamically allocate frequency resources at ground stations while accounting for handover-related data. Simulation results demonstrate that the proposed method effectively reduces communication delay.
Keyword (in Japanese) (See Japanese page) 
(in English) low Earth orbit satellites / handover / frequency resource allocation / multi agent reinforcement learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 359, SAT2025-64, pp. 13-18, Feb. 2026.
Paper # SAT2025-64 
Date of Issue 2026-02-12 (SAT) 
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 SAT2025-64

Conference Information
Committee SAT SANE  
Conference Date 2026-02-19 - 2026-02-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Eef-Jouhou-Plaza 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Satellite Application and General Issues 
Paper Information
Registration To SAT 
Conference Code 2026-02-SAT-SANE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Multi-Agent Reinforcement Learning Approach for Wide-Area Optimization of Frequency Resource Allocation in Satellite-Ground Links 
Sub Title (in English)  
Keyword(1) low Earth orbit satellites  
Keyword(2) handover  
Keyword(3) frequency resource allocation  
Keyword(4) multi agent reinforcement learning  
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1st Author's Name Nao Kubota  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
2nd Author's Name Hiroaki Hashida  
2nd Author's Affiliation Tohoku University (Tohoku Univ.)
3rd Author's Name Yuichi Kawamoto  
3rd Author's Affiliation Tohoku University (Tohoku Univ.)
4th Author's Name Nei Kato  
4th Author's Affiliation Tohoku University (Tohoku Univ.)
5th Author's Name Yohei Hasegawa  
5th Author's Affiliation NEC Advanced Network Research Laboratories (NEC)
6th Author's Name Masayuki Ariyoshi  
6th Author's Affiliation NEC Advanced Network Research Laboratories (NEC)
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Speaker Author-1 
Date Time 2026-02-19 09:10:00 
Presentation Time 20 minutes 
Registration for SAT 
Paper # SAT2025-64 
Volume (vol) vol.125 
Number (no) no.359 
Page pp.13-18 
#Pages 6 
Date of Issue 2026-02-12 (SAT) 

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