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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
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  
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)
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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 
Date of Issue 2021-07-08 (CS) 

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