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Paper Abstract and Keywords
Presentation 2021-03-05 09:00
Adaptive Channel Prediction over Multi-Cluster and Time-Varying Channels for Analog-Digital-Hybrid Massive MIMO Systems
Kenta Tsuge, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech), Satoshi Suyama, Takahiro Asai (NTT DOCOMO) RCS2020-252
Abstract (in Japanese) (See Japanese page) 
(in English) For 5G massive MIMO systems, hybrid beamforming (HB), which is composed of the analog beamforming (AB) and the digital beamforming (DB), has been investigated to reduce the number of baseband and RF circuits. This report proposes a scheme to adaptively predict downlink (DL) channels based on multi-cluster and time-varying channel models. For time division duplex (TDD) multiuser MIMO communications, the proposed scheme firstly applies both spatial smoothing processing and multiple signal classification (MUSIC) to an estimated channel impulse response of each stream, in order to estimate an average angle of arrival (AoA) for each cluster per stream. Next, the uplink (UL) channels are estimated in the following two ways: One is linear interpolation of weight coefficients multiplied by array response vectors corresponding to the estimated average and its neighboring AoAs, where these weight coefficients are estimated by using training signals. The other is QR decomposition-based recursive least squares (QR-RLS), which exploits detected data signals besides the training signals. The DL channels are predicted by linear extrapolation of estimated UL channels that are adaptively selected on the minimum mean square error (MMSE) criterion. Computer simulations demonstrate that the proposed scheme can achieve DL average bit error rate (BER) performances that are little inferior to those of full DB.
Keyword (in Japanese) (See Japanese page) 
(in English) 5G / massive MIMO / hybrid beamforming / channel estimation / MUSIC / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 404, RCS2020-252, pp. 222-227, March 2021.
Paper # RCS2020-252 
Date of Issue 2021-02-24 (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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Conference Information
Committee RCS SR SRW  
Conference Date 2021-03-03 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2021-03-RCS-SR-SRW 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Adaptive Channel Prediction over Multi-Cluster and Time-Varying Channels for Analog-Digital-Hybrid Massive MIMO Systems 
Sub Title (in English)  
Keyword(1) 5G  
Keyword(2) massive MIMO  
Keyword(3) hybrid beamforming  
Keyword(4) channel estimation  
Keyword(5) MUSIC  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Kenta Tsuge  
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 Satoshi Suyama  
4th Author's Affiliation NTT DOCOMO, INC. (NTT DOCOMO)
5th Author's Name Takahiro Asai  
5th Author's Affiliation NTT DOCOMO, INC. (NTT DOCOMO)
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Speaker Author-1 
Date Time 2021-03-05 09:00:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2020-252 
Volume (vol) vol.120 
Number (no) no.404 
Page pp.222-227 
#Pages 6 
Date of Issue 2021-02-24 (RCS) 

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