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Paper Abstract and Keywords
Presentation 2023-07-13 16:25
[Short Paper] A study of Cross-Layer Adaptation using Learning in Wireless MAC Protocols
Koshiro Aruga, Takeo Fujii (UEC) SR2023-40
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, with the development of wireless communication technology, networks have become larger and more complex. Until now, the protocols and parameters used in networks have been determined by humans based on their experience, but this has led to the problem of performance degradation due to the inability to adapt to changes in the environment. To solve this problem, a wireless MAC protocol is needed that can adapt to various environments and applications by allowing the network to control itself autonomously.The proposed method collects and learns information about each node in the gateway (GW), and selects parameters for Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) according to the environment. In addition, the system selects the parameters on each node according to the environment. In addition, assuming an environment in which a video viewing application using adaptive bitrate (ABR) is running on each node, the proposed method improves QoE by selecting an appropriate bitrate. We show that the proposed method improves the average throughput and QoE compared to CSMA/CA without parameter adjustment. To evaluate the performance of the proposed method, we compared it with CSMA/CA without parameter tuning. Computer simulation was used for the comparison, and the number of nodes communicating simultaneously was varied. The simulation results show that the proposed method achieves higher average throughput and QoE than the protocol without parameter adjustment.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Reinforcement Learning / DQN / Network Protocol / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 109, SR2023-40, pp. 55-57, July 2023.
Paper # SR2023-40 
Date of Issue 2023-07-05 (SR) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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 SR2023-40

Conference Information
Committee SeMI RCS RCC NS SR  
Conference Date 2023-07-12 - 2023-07-14 
Place (in Japanese) (See Japanese page) 
Place (in English) Osaka University Nakanoshima Center + Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Distributed Wireless Network, M2M (Machine-to-Machine),D2D (Device-to-Device),IoT(Internet of Things), etc 
Paper Information
Registration To SR 
Conference Code 2023-07-SeMI-RCS-RCC-NS-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of Cross-Layer Adaptation using Learning in Wireless MAC Protocols 
Sub Title (in English)  
Keyword(1) Deep Reinforcement Learning  
Keyword(2) DQN  
Keyword(3) Network Protocol  
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1st Author's Name Koshiro Aruga  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Takeo Fujii  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2023-07-13 16:25:00 
Presentation Time 15 minutes 
Registration for SR 
Paper # SR2023-40 
Volume (vol) vol.123 
Number (no) no.109 
Page pp.55-57 
#Pages
Date of Issue 2023-07-05 (SR) 


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