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Presentation 2022-01-21 09:40
[Short Paper] Study of ACK-Less Rate Adaptation for IEEE 802.11bc Using Deep Reinforcement Learning
Takamochi Kanda (Kyoto Univ.), Yusuke Koda (Univ. of Oulu), Yuto Kihira, Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.) SeMI2021-74
Abstract (in Japanese) (See Japanese page) 
(in English) This paper introduces an ACK-less rate adaptation to locational variation of recipient stations (STAs) for broadcast wireless local area networks (WLANs). It is basically challenging in broadcast WLANs to adapt transmit rate to locational variation in recipient STAs owing to the lack of acknowledgement (ACK) mechanisms from STAs. Our key idea is that the broadcast access point (AP) control the rate based on received signal strength (RSS) of uplink frames transmitted by a partial recipient STAs to non-broadcast APs. Hence, by exploiting the RSS of the uplink frames, the broadcast AP can learn the statistics of the performance, thereby performing the ACK-less adaptation to the various STA deployments. For learning the statistics, we apply deep reinforcement learning. Simulations confirm that learning the expected value of the indicator enables the broadcast AP to alleviate the reception failures while keeping the transmit rate higher.
Keyword (in Japanese) (See Japanese page) 
(in English) IEEE 802.11bc / broadcast WLAN / deep reinforcement learning / rate control / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 333, SeMI2021-74, pp. 86-88, Jan. 2022.
Paper # SeMI2021-74 
Date of Issue 2022-01-13 (SeMI) 
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 SeMI  
Conference Date 2022-01-20 - 2022-01-21 
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Paper Information
Registration To SeMI 
Conference Code 2022-01-SeMI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study of ACK-Less Rate Adaptation for IEEE 802.11bc Using Deep Reinforcement Learning 
Sub Title (in English)  
Keyword(1) IEEE 802.11bc  
Keyword(2) broadcast WLAN  
Keyword(3) deep reinforcement learning  
Keyword(4) rate control  
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1st Author's Name Takamochi Kanda  
1st Author's Affiliation Kyoto University (Kyoto Univ.)
2nd Author's Name Yusuke Koda  
2nd Author's Affiliation University of Oulu (Univ. of Oulu)
3rd Author's Name Yuto Kihira  
3rd Author's Affiliation Kyoto University (Kyoto Univ.)
4th Author's Name Koji Yamamoto  
4th Author's Affiliation Kyoto University (Kyoto Univ.)
5th Author's Name Takayuki Nishio  
5th Author's Affiliation Kyoto University/Tokyo Institute of Technology (Kyoto Univ./Tokyo Tech.)
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Speaker Author-1 
Date Time 2022-01-21 09:40:00 
Presentation Time 10 minutes 
Registration for SeMI 
Paper # SeMI2021-74 
Volume (vol) vol.121 
Number (no) no.333 
Page pp.86-88 
#Pages
Date of Issue 2022-01-13 (SeMI) 


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