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Paper Abstract and Keywords
Presentation 2026-05-12 15:05
Implementation and Performance Evaluation of an Autonomous Distributed Channel Allocation Method Based on Deep Reinforcement Learning
Miyu Tsuzuki, Aohan Li (UEC) SR2026-16
Abstract (in Japanese) (See Japanese page) 
(in English) Long Range (LoRa) is a type of Low Power Wide Area (LPWA) technology, characterised by low power consumption and long-range communication. The number of Internet of Things (IoT) devices has increased dramatically in recent years, leading to challenges such as collisions, delays, and increased power consumption. Consequently, it is necessary to dynamically select transmission parameters such as the channel (CH). While LoRa's transmission parameter selection methods include the Multi-Armed Bandit (MAB) algorithm and Reinforcement Learning (RL), their adaptability may be limited in more complex, real-world environments. Deep Reinforcement Learning, utilising Deep Neural Networks (DNNs), can adapt to complex environments and is thus employed as an approach for resource allocation (RA) methods across numerous IoT devices. This paper proposes a deep reinforcement learning-based learning approach adapted for practical implementation in LoRa networks. The proposed method selects transmission parameters (channels) based on information from previously selected channels, ACK (Acknowledgment) information, Main Network values, and action selection probabilities, then iterates learning from the results. This study implemented the proposed method on LoRa devices to demonstrate its effectiveness, and conducted performance evaluations and analyses of the learning parameters. The experimental results demonstrated that the proposed learning method is effective, and that learning becomes more stable by adjusting the parameter update method for the probability of action selection during the learning process.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / LoRa / Communication Success Rate / Transmission Parameter Selection / Deep Reinforcement Learning / Autonomous Distributed Control / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 13, SR2026-16, pp. 78-83, May 2026.
Paper # SR2026-16 
Date of Issue 2026-05-04 (SR) 
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 SR2026-16

Conference Information
Committee SR  
Conference Date 2026-05-11 - 2026-05-12 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) IoT，general 
Paper Information
Registration To SR 
Conference Code 2026-05-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Implementation and Performance Evaluation of an Autonomous Distributed Channel Allocation Method Based on Deep Reinforcement Learning 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) LoRa  
Keyword(3) Communication Success Rate  
Keyword(4) Transmission Parameter Selection  
Keyword(5) Deep Reinforcement Learning  
Keyword(6) Autonomous Distributed Control  
Keyword(7)  
Keyword(8)  
1st Author's Name Miyu Tsuzuki  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Aohan Li  
2nd Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2026-05-12 15:05:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2026-16 
Volume (vol) vol.126 
Number (no) no.13 
Page pp.78-83 
#Pages 6 
Date of Issue 2026-05-04 (SR) 

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