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Paper Abstract and Keywords
Presentation 2022-10-14 10:40
[Poster Presentation] A New CSI Feedback with Quantization Based on Adaptive DNN for FDD Massive MIMO Systems
Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki (Keio Univ.), Guan Gui (NJUPT)
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.
Paper #  
Date of Issue  
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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Conference Information
Committee MIKA  
Conference Date 2022-10-12 - 2022-10-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Niigata Citizens Plaza 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MIKA 
Conference Code 2022-10-MIKA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A New CSI Feedback with Quantization Based on Adaptive DNN 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  
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 Nanjing University of Posts and Telecommunications (NJUPT)
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Speaker Author-1 
Date Time 2022-10-14 10:40:00 
Presentation Time 50 minutes 
Registration for MIKA 
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