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Paper Abstract and Keywords
Presentation 2021-10-07 11:40
Wireless Channel Prediction with Gaussian Process
Yitu Wang (NTT), Takayuki Nakachi (former NTT), Takeru Inoue, Toru Mano, Kudo Riichi (NTT) SIS2021-12
Abstract (in Japanese) (See Japanese page) 
(in English) With accurate knowledge of future Channel State Information (CSI), it becomes possible to better manipulate the wireless resources in a proactive manner, so as to provide solid support to smart and high quality wireless transmission. However, in mobile environment, the evolving correlation patterns in CSI series challenge the existing data-driven algorithms to adaptively learn and predict its behavior. In this article, an adaptive learning algorithm is proposed based on Gaussian Process (GP), to discover and utilize the spatial correlation within a channel and across channels, and produce accurate CSI prediction. Specifically,
1). To track the evolving correlation of a channel, we tailor Spectrum Mixture (SM) kernel to not only approximate the optimal kernel adapting to the current CSI, but also capture the combined effect of path loss and User Equipment (UE) motion.
2). The correlation across channels is encoded into the GP-based learning framework through Linear Model of Co-regionalization (LMC).
Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes such as stochastic based ARIMA, deep learning based LSTM, etc.
Keyword (in Japanese) (See Japanese page) 
(in English) Channel State Information / Prediction Algorithm / Gaussian Process / Linear model of coreginalization / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 190, SIS2021-12, pp. 11-16, Oct. 2021.
Paper # SIS2021-12 
Date of Issue 2021-09-30 (SIS) 
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)
Download PDF SIS2021-12

Conference Information
Committee SIS ITE-BCT  
Conference Date 2021-10-07 - 2021-10-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) System Implementation Technology, Short Range Wireless Systems, Smart Multimedia Systems, Broadcasting Technology, etc. 
Paper Information
Registration To SIS 
Conference Code 2021-10-SIS-BCT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Wireless Channel Prediction with Gaussian Process 
Sub Title (in English)  
Keyword(1) Channel State Information  
Keyword(2) Prediction Algorithm  
Keyword(3) Gaussian Process  
Keyword(4) Linear model of coreginalization  
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1st Author's Name Yitu Wang  
1st Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
2nd Author's Name Takayuki Nakachi  
2nd Author's Affiliation University of the Ryukyus (former NTT)
3rd Author's Name Takeru Inoue  
3rd Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
4th Author's Name Toru Mano  
4th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
5th Author's Name Kudo Riichi  
5th Author's Affiliation Nippon Telegraph and Telephone Corporation (NTT)
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Speaker Author-1 
Date Time 2021-10-07 11:40:00 
Presentation Time 20 minutes 
Registration for SIS 
Paper # SIS2021-12 
Volume (vol) vol.121 
Number (no) no.190 
Page pp.11-16 
#Pages 6 
Date of Issue 2021-09-30 (SIS) 

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