Multi-Rate Compression for Downlink CSI Based on Transfer Learning in FDD Massive MIMO Systems

Yuting Wang, Jinlong Sun, Jie Wang, Jie Yang, Tomoaki Ohtsuki, Bamidele Adebisi, Haris Gacanin

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

1 Citation (Scopus)


Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

Original languageEnglish
Title of host publication2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665413688
Publication statusPublished - 2021
Event94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
Duration: 2021 Sept 272021 Sept 30

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Country/TerritoryUnited States
CityVirtual, Online


  • Downlink CSI feedback
  • deep learning
  • massive MIMO
  • transfer learning

ASJC Scopus subject areas

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


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