Paper Abstract and Keywords |
Presentation |
2021-07-16 09:40
Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However, many overlapping cells cause inter-cell interference (ICI), which can damage the improvement of the system capacity. As one of ICI coordination (ICIC) to compensate for the damage, base stations (BSs) control both transmit power and transmit beamforming. Since this kind of ICIC can be regarded as an optimization problem, a conventional scheme conducts exhaustive search (ES) in order to choose the optimal combination of transmit power levels and precoding matrices from a pre-defined codebook. However, ES requires a prohibitive amount of computational complexity that grows exponentially with the number of BSs, and thus can not be applied to a large scale system. To reduce the complexity, this report applies a convolutional neural network (CNN) into the ICIC. The reason for adopting CNN is that CNN requires a small amount of computational complexity for predicting optimal values, although its training process needs a large amount of complexity. Machine learning for CNN can be mainly classified into supervised and unsupervised learning. Since the supervised learning needs results of ES as the training sequence, it is very difficult to adopt the supervised learning in case of a large scale system. Therefore, this report proposes CNN employing unsupervised learning for the ICIC. Computer simulations under MIMO communications with 3 cells having 3-sector antennas clarify that the proposed scheme can improve the system capacity drastically while requiring a less amount of complexity. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
MIMO / inter-cell interference coordination / transmit power control / beamforming control / convolutional neural network / unsupervised learning / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 113, CS2021-29, pp. 63-68, July 2021. |
Paper # |
CS2021-29 |
Date of Issue |
2021-07-08 (CS) |
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 |
CS2021-29 |
Conference Information |
Committee |
CS |
Conference Date |
2021-07-15 - 2021-07-16 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Next Generation Networks, Access Networks, Broadband Access, Power Line Communications, Wireless Communication Systems, Coding Systems, etc. |
Paper Information |
Registration To |
CS |
Conference Code |
2021-07-CS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks |
Sub Title (in English) |
|
Keyword(1) |
MIMO |
Keyword(2) |
inter-cell interference coordination |
Keyword(3) |
transmit power control |
Keyword(4) |
beamforming control |
Keyword(5) |
convolutional neural network |
Keyword(6) |
unsupervised learning |
Keyword(7) |
|
Keyword(8) |
|
1st Author's Name |
Naoto Tamada |
1st Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
2nd Author's Name |
Yuyuan Chang |
2nd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
3rd Author's Name |
Kazuhiko Fukawa |
3rd Author's Affiliation |
Tokyo Institute of Technology (Tokyo Tech) |
4th Author's Name |
|
4th Author's Affiliation |
() |
5th Author's Name |
|
5th Author's Affiliation |
() |
6th Author's Name |
|
6th Author's Affiliation |
() |
7th Author's Name |
|
7th Author's Affiliation |
() |
8th Author's Name |
|
8th Author's Affiliation |
() |
9th Author's Name |
|
9th Author's Affiliation |
() |
10th Author's Name |
|
10th Author's Affiliation |
() |
11th Author's Name |
|
11th Author's Affiliation |
() |
12th Author's Name |
|
12th Author's Affiliation |
() |
13th Author's Name |
|
13th Author's Affiliation |
() |
14th Author's Name |
|
14th Author's Affiliation |
() |
15th Author's Name |
|
15th Author's Affiliation |
() |
16th Author's Name |
|
16th Author's Affiliation |
() |
17th Author's Name |
|
17th Author's Affiliation |
() |
18th Author's Name |
|
18th Author's Affiliation |
() |
19th Author's Name |
|
19th Author's Affiliation |
() |
20th Author's Name |
|
20th Author's Affiliation |
() |
Speaker |
Author-1 |
Date Time |
2021-07-16 09:40:00 |
Presentation Time |
10 minutes |
Registration for |
CS |
Paper # |
CS2021-29 |
Volume (vol) |
vol.121 |
Number (no) |
no.113 |
Page |
pp.63-68 |
#Pages |
6 |
Date of Issue |
2021-07-08 (CS) |
|