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Paper Abstract and Keywords
Presentation 2023-08-31 10:30
Enhancing CSI Feedback in FDD Massive MIMO Systems using Dropout-based Deep Neural Network
Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.), Guan Gui (NJUPT) RCS2023-101
Abstract (in Japanese) (See Japanese page) 
(in English) Accessing precise downlink channel state information (CSI) is crucial in maximizing the
benefits of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO)
systems, as they lack strong channel reciprocity. However, this requires continuous CSI
feedback, leading to significant computational burdens. Existing compressive sensing
(CS)-based and deep learning (DL)-based methods have attempted to address these
challenges, but have not achieved the desired level of CSI feedback or overhead reduction. To overcome these limitations, a dropout-based deep neural network (DNN) is proposed in this
paper. Simulation results demonstrate that the proposed method outperforms conventional
approaches in terms of normalized mean square error (NMSE), even with a limited dataset in
some noisy scenarios. These findings highlight the efficacy of the proposed method in
improving CSI reconstruction accuracy and reducing the demand for training data, which
enhance the robustness of the entire system.
Keyword (in Japanese) (See Japanese page) 
(in English) CSI feedback / deep neural network / classification / quantization / dropout / Massive MIMO / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 172, RCS2023-101, pp. 1-4, Aug. 2023.
Paper # RCS2023-101 
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 RCS2023-101

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 English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhancing CSI Feedback in FDD Massive MIMO Systems using Dropout-based Deep Neural Network 
Sub Title (in English)  
Keyword(1) CSI feedback  
Keyword(2) deep neural network  
Keyword(3) classification  
Keyword(4) quantization  
Keyword(5) dropout  
Keyword(6) Massive MIMO  
Keyword(7)  
Keyword(8)  
1st Author's Name Junjie Gao  
1st Author's Affiliation Keio University (Keio Univ.)
2nd Author's Name Mondher Bouazizi  
2nd Author's Affiliation Keio University (Keio Univ.)
3rd Author's Name Tomoaki Ohtsuki  
3rd Author's Affiliation Keio University (Keio Univ.)
4th Author's Name Guan Gui  
4th Author's Affiliation NJUPT (NJUPT)
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Speaker Author-1 
Date Time 2023-08-31 10:30:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2023-101 
Volume (vol) vol.123 
Number (no) no.172 
Page pp.1-4 
#Pages 4 
Date of Issue 2023-08-24 (RCS) 

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