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Paper Abstract and Keywords
Presentation 2019-06-19 14:55
Policy Gradient Reinforcement Learning for Reducing Transmission Delay in EDCA
Masao Shinzaki, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.) RCS2019-52
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a packet mapping algorithm among Access Categories (ACs) in Enhanced Distributed Channel Access (EDCA) scheme based on policy gradient Reinforcement Learning (RL).In EDCA scheme based on an autonomous distributed control, high priority packets obtain more transmission opportunity than low priority packets.The arrival rate of high priority packets can be higher than that of low priority packets.In such a situation, mapping high priority packets to the AC defined in EDCA scheme is not necessarily the best mapping algorithm to minimize the transmission delay of high priority packets.
Therefore, we assume that EDCA scheme can map high priority packets to any AC.Although each AP sends high priority packets early, however, APs can not always send high priority packets early.This paper proposes policy gradient RL to empirically obtain optimal mapping algorithm.By using the mapping algorithm based on RL, simulation results reveal that the transmission delay can be reduced.The average transmission delay of the proposed mapping algorithm is 13.8% smaller than that of the conventional mapping algorithm.Moreover, the average transmission delay of the proposed mapping algorithm is 5.2% smaller than that of the heuristic mapping algorithm.
Keyword (in Japanese) (See Japanese page) 
(in English) IEEE 802.11e / EDCA / reinforcement learning / policy gradient / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 90, RCS2019-52, pp. 91-96, June 2019.
Paper # RCS2019-52 
Date of Issue 2019-06-12 (RCS) 
ISSN Online edition: ISSN 2432-6380
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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)
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Conference Information
Committee RCS  
Conference Date 2019-06-19 - 2019-06-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyakojima Hirara Port Terminal Building 
Topics (in Japanese) (See Japanese page) 
Topics (in English) First Presentation in IEICE Technical Committee, Resource Control, Scheduling, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2019-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Policy Gradient Reinforcement Learning for Reducing Transmission Delay in EDCA 
Sub Title (in English)  
Keyword(1) IEEE 802.11e  
Keyword(2) EDCA  
Keyword(3) reinforcement learning  
Keyword(4) policy gradient  
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1st Author's Name Masao Shinzaki  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Yusuke Koda  
2nd Author's Affiliation Kyoto University (Kyoto Univ.)
3rd Author's Name Koji Yamamoto  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Takayuki Nishio  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
5th Author's Name Masahiro Morikura  
5th Author's Affiliation Kyoto University (Kyoto Univ.)
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Speaker Author-1 
Date Time 2019-06-19 14:55:00 
Presentation Time 10 minutes 
Registration for RCS 
Paper # RCS2019-52 
Volume (vol) vol.119 
Number (no) no.90 
Page pp.91-96 
#Pages
Date of Issue 2019-06-12 (RCS) 


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