A novel compression CSI feedback based on deep learning for FDD massive MIMO systems

Yuting Wang, Yibin Zhang, Jinlong Sun, Guan Gui, Tomoaki Ohtsuki, Fumiyuki Adachi

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

2 Citations (Scopus)

Abstract

Accurate channel state information (CSI) is necessary for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. Existing deep learning-based CSI feedback methods, e.g., CSI sensing and recovery neural network (CsiNet), designed based on an autoencoder architecture, achieves higher feedback accuracy and reconstruction speed. However, this network needs to be retrained due to different communication scenarios and channel conditions, which is costly in practical deployment. To solve this problem, this paper proposes a deep learning-based modular adaptive multiple-rate (MAMR) compression CSI feedback framework. Extra padding modules are added at the base station to pad compressed CSI into different compression rates into the same dimensions, thereby realizing a general autoencoder performing variable-rate compression. Simulation results are given to confirm the effectiveness of the proposed method in terms of normalized mean square error.

Original languageEnglish
Title of host publication2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195056
DOIs
Publication statusPublished - 2021
Event2021 IEEE Wireless Communications and Networking Conference, WCNC 2021 - Nanjing, China
Duration: 2021 Mar 292021 Apr 1

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2021-March
ISSN (Print)1525-3511

Conference

Conference2021 IEEE Wireless Communications and Networking Conference, WCNC 2021
Country/TerritoryChina
CityNanjing
Period21/3/2921/4/1

Keywords

  • CSI feedback
  • Compression
  • Deep learning
  • General autoencoder
  • Modular adaptive multiple-rate

ASJC Scopus subject areas

  • Engineering(all)

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