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Paper Abstract and Keywords
Presentation 2021-04-22 10:55
A Study on Deep Learning Based Resource Allocation Method to Control System Capacity and Fairness for MU-MIMO THP
Yukiko Shimbo, Hirofumi Suganuma (Waseda Univ.), Hiromichi Tomeba, Takashi Onodera (Sharp), Fumiaki Maehara (Waseda Univ.) RCS2021-2
Abstract (in Japanese) (See Japanese page) 
(in English) This report proposes a deep-learning-based resource allocation method to adaptively control system capacity and fairness for multi-user multiple-input and multiple-output (MUMIMO). In the proposed method, Tomlinson-Harashima precoding (THP) is used to enhance the transmission rate. Additionally, channel resources are appropriately allocated based on user scheduling techniques, i.e., semiorthogonal user selection (SUS) for throughput maximization and proportional fairness (PF) for
fairness among users. The primary feature of the proposed method is that it appropriately allocates channel resources by utilizing the user position information and target fairness index (FI) through deep learning. This makes it possible to meet various service requirements. Numerical simulations are used to demonstrate the effectiveness of the proposed method in terms of system capacity and fairness under different MIMO configurations and user distributions.
Keyword (in Japanese) (See Japanese page) 
(in English) MU-MIMO / SUS / PF / system capacity / FI / deep learning / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 7, RCS2021-2, pp. 6-10, April 2021.
Paper # RCS2021-2 
Date of Issue 2021-04-15 (RCS) 
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 RCS2021-2

Conference Information
Committee RCS  
Conference Date 2021-04-22 - 2021-04-23 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Railroad Communications, Inter-Vehicle Communications, Road to Vehicle Communications, Radio Access Technologies, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2021-04-RCS 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Deep Learning Based Resource Allocation Method to Control System Capacity and Fairness for MU-MIMO THP 
Sub Title (in English)  
Keyword(1) MU-MIMO  
Keyword(2) SUS  
Keyword(3) PF  
Keyword(4) system capacity  
Keyword(5) FI  
Keyword(6) deep learning  
Keyword(7)  
Keyword(8)  
1st Author's Name Yukiko Shimbo  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Hirofumi Suganuma  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Hiromichi Tomeba  
3rd Author's Affiliation Sharp Corporation (Sharp)
4th Author's Name Takashi Onodera  
4th Author's Affiliation Sharp Corporation (Sharp)
5th Author's Name Fumiaki Maehara  
5th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2021-04-22 10:55:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2021-2 
Volume (vol) vol.121 
Number (no) no.7 
Page pp.6-10 
#Pages 5 
Date of Issue 2021-04-15 (RCS) 

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