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Paper Abstract and Keywords
Presentation 2023-03-01 10:25
A Novel DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems
Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.), Gui Guan (NJUPT) RCS2022-252
Abstract (in Japanese) (See Japanese page) 
(in English) Accessing the accurate downlink channel state information
(CSI) is essential to take full advantage of frequency
division duplex (FDD) massive multiple-input multiple-output
(MIMO) systems due to its weak channel reciprocity. Meanwhile,
great computational burdens will happen, which is accompanied
by continuous CSI feedback. The existing compressive sensing
(CS)-based and deep learning (DL)-based methods try to solve
such problems, but do not achieve desired effect to get ideal CSI
feedback or decrease the overhead. An adaptive deep neural
network (DNN)-based CSI feedback method is proposed in this
paper to address this. A classification block of the compression
ratio is adopted and modified to apply to a more complex
channel model named Clustered-Delay-Line (CDL), which helps
decrease the computational overhead of the network. Besides, the
reconstruction accuracy of the CSI feedback is further improved
by proposing a new structure of the encoder. Quantization and
dequantization modules are also applied to make the whole
network more robust and effectively minimize the quantization
distortion in the real communication scenario, respectively. The
simulation results show that the proposed method performs better
than the conventional ones on the CSI reconstruction accuracy
in terms of normalized mean square error (NMSE), even though
the quantization module is added.
Keyword (in Japanese) (See Japanese page) 
(in English) CSI feedback / deep neural network / classification / quantization / massive MIMO / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 399, RCS2022-252, pp. 31-35, March 2023.
Paper # RCS2022-252 
Date of Issue 2023-02-22 (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 RCS2022-252

Conference Information
Committee RCS SR SRW  
Conference Date 2023-03-01 - 2023-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Tokyo Institute of Technology, and Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Mobile Communication Workshop 
Paper Information
Registration To RCS 
Conference Code 2023-03-RCS-SR-SRW 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Novel DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems 
Sub Title (in English)  
Keyword(1) CSI feedback  
Keyword(2) deep neural network  
Keyword(3) classification  
Keyword(4) quantization  
Keyword(5) massive MIMO  
Keyword(6)  
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 Gui Guan  
4th Author's Affiliation Nanjing University of Posts and Telecommunications (NJUPT)
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Speaker Author-1 
Date Time 2023-03-01 10:25:00 
Presentation Time 25 minutes 
Registration for RCS 
Paper # RCS2022-252 
Volume (vol) vol.122 
Number (no) no.399 
Page pp.31-35 
#Pages 5 
Date of Issue 2023-02-22 (RCS) 

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