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Paper Abstract and Keywords
Presentation 2018-03-01 10:00
CSI Overhead Reduction for Massive MIMO using Multi-Dimensional Scaling Extended in Time-Domain and AR Model
Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.) RCS2017-349
Abstract (in Japanese) (See Japanese page) 
(in English) Massive MIMO (multiple-input multiple-output) is one of the technologies that has been focused in 5G (5th generation mobile communications). However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiving terminal to the base station (BS), due to the enormous number of antennas. For the purpose of solving this issue, there exists the method to compress CSI to a lower dimension matrix by multi-dimensional scaling (MDS). However, this conventional method needs to hold a lot of CSIs at the receiving terminal and predict CSIs considering the delays using them, thus the loads applied to each receiving terminal is large. Besides, the number of dimensions when mapping CSI in the multi-dimensional space depends on the number of receiving antennas. However, because the number of antennas that can be deployed at the receiving terminal is limited, the number of antennas that can be assigned when compressing is limited. In this report, we propose the method that feeds back the CSI after extending in time-domain and compressing and compensates the mismatches of the time change by prediction based on auto regressive (AR) model. In our proposed method, the choices of the eigenvalues that can be adopted are increased by extending the size of the channel matrix, and the all CSI prediction process using AR model is performed at the BS. By computer simulation, we show that our proposed method achieves the higher system capacity compared to the conventional CSI compression method using MDS due to the improvement of the accuracy of CSI compression and restoration.
Keyword (in Japanese) (See Japanese page) 
(in English) Massive MIMO / Overhead Reduction / Multi-Dimensional Scaling / AR Model / Dimensions Reduction / 5G / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 456, RCS2017-349, pp. 185-190, Feb. 2018.
Paper # RCS2017-349 
Date of Issue 2018-02-21 (RCS) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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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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Conference Information
Committee RCS SR SRW  
Conference Date 2018-02-28 - 2018-03-02 
Place (in Japanese) (See Japanese page) 
Place (in English) YRP 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2018-02-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) CSI Overhead Reduction for Massive MIMO using Multi-Dimensional Scaling Extended in Time-Domain and AR Model 
Sub Title (in English)  
Keyword(1) Massive MIMO  
Keyword(2) Overhead Reduction  
Keyword(3) Multi-Dimensional Scaling  
Keyword(4) AR Model  
Keyword(5) Dimensions Reduction  
Keyword(6) 5G  
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1st Author's Name Rei Nagashima  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Tomoaki Ohtsuki  
2nd Author's Affiliation Keio University (Keio Univ.)
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Speaker Author-1 
Date Time 2018-03-01 10:00:00 
Presentation Time 20 minutes 
Registration for RCS 
Paper # RCS2017-349 
Volume (vol) vol.117 
Number (no) no.456 
Page pp.185-190 
#Pages
Date of Issue 2018-02-21 (RCS) 


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