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Paper Abstract and Keywords
Presentation 2024-01-25 13:25
[Short Paper] A Performance Evaluation on Deep Reinforcement Learning based Transmit Power Control for Uplink NOMA
Kaito Sawada, Xiaoyan Wang, Zhou Hang (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.) SR2023-73
Abstract (in Japanese) (See Japanese page) 
(in English) Non-Orthogonal Multiple Access (NOMA) technology has attracted much attention in order to improve frequency utilization efficiency. In this paper, with the purpose of maximizing the throughput in uplink NOMA , we investigate a transmission power control method based on deep reinforcement learning technique in a scenario with multiple user equipment and a signal base station. We evaluate the performance by comparing with ideal results and random power control methods in wireless environments with both known and unknown channel information.
Keyword (in Japanese) (See Japanese page) 
(in English) Non-Orthogonal Multiple Access / uplink / deep reinforcement learning / transmit power control / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 369, SR2023-73, pp. 25-27, Jan. 2024.
Paper # SR2023-73 
Date of Issue 2024-01-18 (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 SR2023-73

Conference Information
Committee SR  
Conference Date 2024-01-25 - 2024-01-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagano-ken JA building 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Cognitive Radio, Dynamic Spectrum Sharing, Heterogeneous Network, SDN, AI/ML application 
Paper Information
Registration To SR 
Conference Code 2024-01-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Performance Evaluation on Deep Reinforcement Learning based Transmit Power Control for Uplink NOMA 
Sub Title (in English)  
Keyword(1) Non-Orthogonal Multiple Access  
Keyword(2) uplink  
Keyword(3) deep reinforcement learning  
Keyword(4) transmit power control  
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1st Author's Name Kaito Sawada  
1st Author's Affiliation Ibaraki University (Ibaraki Univ.)
2nd Author's Name Xiaoyan Wang  
2nd Author's Affiliation Ibaraki University (Ibaraki Univ.)
3rd Author's Name Zhou Hang  
3rd Author's Affiliation Ibaraki University (Ibaraki Univ.)
4th Author's Name Masahiro Umehira  
4th Author's Affiliation Nanzan University (Nanzan Univ.)
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Speaker Author-1 
Date Time 2024-01-25 13:25:00 
Presentation Time 15 minutes 
Registration for SR 
Paper # SR2023-73 
Volume (vol) vol.123 
Number (no) no.369 
Page pp.25-27 
#Pages 3 
Date of Issue 2024-01-18 (SR) 

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