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) |
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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) |
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Keyword(1) |
MU-MIMO |
Keyword(2) |
SUS |
Keyword(3) |
PF |
Keyword(4) |
system capacity |
Keyword(5) |
FI |
Keyword(6) |
deep learning |
Keyword(7) |
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Keyword(8) |
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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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