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Paper Abstract and Keywords
Presentation 2023-08-31 10:55
A study on source data and decoder of multitask CSI feedback method in FDD Massive MIMO
Mayuko Inoue, Tomoaki Ohtsuki (Keio Univ.) RCS2023-102
Abstract (in Japanese) (See Japanese page) 
(in English) In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems, it is necessary to obtain downlink Channel State Information (CSI) at the base station (BS). Previously, a downlink CSI feedback method combining deep transfer learning (DTL) and multi-task learning has been proposed. In this method, an autoencoder is trained with an equal mixture of CSI datasets from different channel environments (source data). Then, only the decoder at the BS is fine-tuned using a small number of CSI datasets from the target channel environment (target data). This approach lowers the storage usage due to encoder placement in the user equipment (UE). In this research, we evaluated the CSI reconstruction performance in the multi-task CSI feedback method by changing mixing ratios of CSI for different channel environments in the source data. The evaluated channel environment is the CDL (Clustered Delay Line) channel model, consisting of five types: CDL-A through CDL-E. From the simulation results, we confirmed that there is a mixing method that achieves better CSI reconstruction accuracy in each CDL channel than the conventional method of equally mixing CSI datasets from the five different CDL channels. Specifically, when using a mixing ratio of CDL-A:CDL-B:CDL-C:CDL-D:CDL-E = 3:3:2:1:1, high CSI reconstruction accuracy was achieved.
Keyword (in Japanese) (See Japanese page) 
(in English) CSI Feedback / Deep Transfer Learning / multi-task learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 172, RCS2023-102, pp. 5-8, Aug. 2023.
Paper # RCS2023-102 
Date of Issue 2023-08-24 (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)
Download PDF RCS2023-102

Conference Information
Committee RCS SAT  
Conference Date 2023-08-31 - 2023-09-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Naganoken Nokyo Building, and online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Satellite Communications, Broadcasting, Forward Error Correction, Wireless Communications, etc. 
Paper Information
Registration To RCS 
Conference Code 2023-08-RCS-SAT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study on source data and decoder of multitask CSI feedback method in FDD Massive MIMO 
Sub Title (in English)  
Keyword(1) CSI Feedback  
Keyword(2) Deep Transfer Learning  
Keyword(3) multi-task learning  
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1st Author's Name Mayuko Inoue  
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 2023-08-31 10:55:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2023-102 
Volume (vol) vol.123 
Number (no) no.172 
Page pp.5-8 
#Pages 4 
Date of Issue 2023-08-24 (RCS) 

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