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Paper Abstract and Keywords
Presentation 2020-06-24 13:50
Investigation of Adaptive Transmission Rate Control Based on Machine Learning Using Only ACK/NAK Feedback
Takumi Endo, Kenichi Higuchi (Tokyo Univ. of Science) RCS2020-25
Abstract (in Japanese) (See Japanese page) 
(in English) This paper investigates an adaptive transmission rate control based on machine learning using only ACK/NAK feedback information. In the conventional approaches, the transmission rate is determined at the transmitter based on the feedback of channel quality information (CQI) from the receiver. However, as the number of terminal increases in the future radio access, the overhead of radio resource required for the CQI feedback may impact on the overall system performance. To address this issue, we consider an adaptive transmission rate control relaying only on the one-bit ACK/NAK feedback which is essentially used for hybrid automatic repeat request (HARQ) to indicating whether the received packet is correctly decoded or not. It should be noted that in the real system, the online adaptation of the transmission rate control for accommodating the changes of the channel conditions in time such as signal-to-noise ratio (SNR) and channel fading rate due to the mobility of the user terminal. Considering these issues, the proposed method is based on the long short-term memory (LSTM) which is categorized to the recurrent neural network (RNN) with additional use of probabilistic random transmission rate selection or transmission rate correction when successive ACK receptions are observed for reducing the negative effect of uncertainty in online learning based on ACK/NAK feedback. The computer simulation results show that the proposed method using only one-bit ACK/NAK feedback can achieve quite comparable throughput performance as in the conventional CQI-based method.
Keyword (in Japanese) (See Japanese page) 
(in English) Transmission rate control / machine learning / LSTM / ARQ / feedback information / online control / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 74, RCS2020-25, pp. 13-18, June 2020.
Paper # RCS2020-25 
Date of Issue 2020-06-17 (RCS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 RCS2020-25

Conference Information
Committee RCS  
Conference Date 2020-06-24 - 2020-06-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
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 2020-06-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of Adaptive Transmission Rate Control Based on Machine Learning Using Only ACK/NAK Feedback 
Sub Title (in English)  
Keyword(1) Transmission rate control  
Keyword(2) machine learning  
Keyword(3) LSTM  
Keyword(4) ARQ  
Keyword(5) feedback information  
Keyword(6) online control  
1st Author's Name Takumi Endo  
1st Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
2nd Author's Name Kenichi Higuchi  
2nd Author's Affiliation Tokyo University of Science (Tokyo Univ. of Science)
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Date Time 2020-06-24 13:50:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-RCS2020-25 
Volume (vol) IEICE-120 
Number (no) no.74 
Page pp.13-18 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2020-06-17 

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