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Paper Abstract and Keywords
Presentation 2017-11-08 16:20
Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction
Rei Nagashima, Tomoaki Ohtsuki (Keio Univ.), Wenjie Jiang, Yasushi Takatori (NTT) RCS2017-221
Abstract (in Japanese) (See Japanese page) 
(in English) Massive MIMO (multiple-input multiple-output) is one of the key technologies to realize 5G (5th Generation). However, there exists an issue such as the increase of the amount of feedback of channel state information (CSI) from the receiver to the transmitter, due to the enormous number of antennas. For the purpose of solving this issue, there exists the technique to compress CSI to a lower dimension matrix and decrease the amount of feedback, by principal component analysis (PCA). In the conventional method, the compression matrix to compress a channel matrix is calculated on the basis of PCA, and the compressed channel is fed back from the receiver to the base station (BS). However, it is necessary to feed back the compression matrix to compress a channel once every updating interval $T_s$ where the compression matrix accounts for a large portion of the amount of feedback. Therefore, there exists a problem that the amount of feedback increases when $T_s$ is small. In this report, to solve this problem, we propose a method to compress the channel matrix based on multi-dimensional scaling (MDS) with channel prediction. In the proposed method, we decrease the size of the CSI by mapping the channels on the multi-dimensional space and reducing the number of dimensions using MDS, thus our method does not need generating and feeding back the compression matrix. By computer simulation, we show that the proposed method achieves the same system capacity with the smaller amount of feedback compared to the conventional one based on PCA when the channel changes fast.
Keyword (in Japanese) (See Japanese page) 
(in English) Massive MIMO / 5G / Channel Compression / Multi-Dimensional Scaling / Channel Prediction / Feedback Reduction / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 284, RCS2017-221, pp. 93-98, Nov. 2017.
Paper # RCS2017-221 
Date of Issue 2017-11-01 (RCS) 
ISSN Print edition: ISSN 0913-5685  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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF RCS2017-221

Conference Information
Committee AP RCS  
Conference Date 2017-11-08 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Adaptive Antenna, Equalization, Interference Canceler, MIMO, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2017-11-AP-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Channel Compression for Massive MIMO based on Multi-Dimensional Scaling with Channel Prediction 
Sub Title (in English)  
Keyword(1) Massive MIMO  
Keyword(2) 5G  
Keyword(3) Channel Compression  
Keyword(4) Multi-Dimensional Scaling  
Keyword(5) Channel Prediction  
Keyword(6) Feedback Reduction  
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.)
3rd Author's Name Wenjie Jiang  
3rd Author's Affiliation NTT Network Innovation Laboratories (NTT)
4th Author's Name Yasushi Takatori  
4th Author's Affiliation NTT Network Innovation Laboratories (NTT)
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Date Time 2017-11-08 16:20:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-RCS2017-221 
Volume (vol) IEICE-117 
Number (no) no.284 
Page pp.93-98 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2017-11-01 

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