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Paper Abstract and Keywords
Presentation 2026-05-14 10:20
Improving TCP Fairness Based on Queue Control with Deep Reinforcement Learning in Network Devices
Shunta Kimura, Saneyasu Yamaguchi (Kogakuin Univ.) NS2026-20
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a queue control method based on deep reinforcement learning (DRL) to mitigate throughput unfairness occurring between CUBIC TCP and TCP BBR when sharing a bottleneck link. CUBIC TCP is characterized by reducing its window size upon packet loss, whereas TCP BBR estimates bandwidth based on round-trip propagation time. In environments where these two protocols coexist, throughput fairness significantly deteriorates. The proposed method monitors the number of packets for each congestion control algorithm within the kernel space of the queuing machine. Subsequently, a DRL program in the user space dynamically selects either packet dropping (DROP) or packet reordering (REVERSE) for specific packets to control queue length and occupancy. Experimental results demonstrate that the application of reinforcement learning improves the throughput ratio and enhances fairness between connections.
Keyword (in Japanese) (See Japanese page) 
(in English) throughput / CUBIC TCP / TCP BBR / Reinforcement Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 21, NS2026-20, pp. 16-21, May 2026.
Paper # NS2026-20 
Date of Issue 2026-05-07 (NS) 
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 NS2026-20

Conference Information
Committee NS  
Conference Date 2026-05-14 - 2026-05-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Hakata Kindai Building + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) High level protocol, Networking technologies (IP and high-layer routing/filtering, Multicast, Quality/Routing control), IP network application technologies (P2P, P4P, Overlay, SIP, NGN), Network system related technologies (System configuration, Interface, Architecture, Hardware/Software/Middleware), Security, Blockchain etc. 
Paper Information
Registration To NS 
Conference Code 2026-05-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improving TCP Fairness Based on Queue Control with Deep Reinforcement Learning in Network Devices 
Sub Title (in English)  
Keyword(1) throughput  
Keyword(2) CUBIC TCP  
Keyword(3) TCP BBR  
Keyword(4) Reinforcement Learning  
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1st Author's Name Shunta Kimura  
1st Author's Affiliation Kogakuin University (Kogakuin Univ.)
2nd Author's Name Saneyasu Yamaguchi  
2nd Author's Affiliation Kogakuin University (Kogakuin Univ.)
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Speaker Author-1 
Date Time 2026-05-14 10:20:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2026-20 
Volume (vol) vol.126 
Number (no) no.21 
Page pp.16-21 
#Pages 6 
Date of Issue 2026-05-07 (NS) 

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