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Paper Abstract and Keywords
Presentation 2020-11-25 09:50
Deep Learning Aided Channel Estimation for Massive MIMO with Pilot Contamination
Hiroki Hirose, Tomoaki Ohtsuki (Keio Univ.) RCS2020-110
Abstract (in Japanese) (See Japanese page) 
(in English) In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) needs accurate estimation of channel state information (CSI) for a user terminal (UT). Due to the time-varying nature of the channel, the length of pilot signals is limited and the number of orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by pilot contamination from the neighboring cells. With the minimum mean square error (MMSE) channel estimation, the influence of pilot contamination can be reduced by the fully known covariance matrix of channels for all the UTs using the same pilot signal. However, this matrix is unknown to the BS a priori, and has to be estimated. In this report, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. In terms of the speed of training, the former method is better than the latter one. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and the covariance estimation method via normalized mean square error (NMSE) of the channel.
Keyword (in Japanese) (See Japanese page) 
(in English) Massive MIMO / Pilot contamination / Channel estimation / Deep learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 249, RCS2020-110, pp. 1-6, Nov. 2020.
Paper # RCS2020-110 
Date of Issue 2020-11-18 (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 AP UWT  
Conference Date 2020-11-25 - 2020-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
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 2020-11-RCS-AP-UWT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Deep Learning Aided Channel Estimation for Massive MIMO with Pilot Contamination 
Sub Title (in English)  
Keyword(1) Massive MIMO  
Keyword(2) Pilot contamination  
Keyword(3) Channel estimation  
Keyword(4) Deep learning  
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1st Author's Name Hiroki Hirose  
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
Date Time 2020-11-25 09:50:00 
Presentation Time 25 
Registration for RCS 
Paper # IEICE-RCS2020-110 
Volume (vol) IEICE-120 
Number (no) no.249 
Page pp.1-6 
#Pages IEICE-6 
Date of Issue IEICE-RCS-2020-11-18 


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