Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

Junjie Gao, Mondher Bouazizi, Tomoaki Ohtsuki, Guan Gui

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454681
Publication statusPublished - 2022
Event96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom
Duration: 2022 Sept 262022 Sept 29

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom


  • classification
  • CSI feedback
  • deep neural network
  • massive MIMO
  • quantization

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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