Joint Channel Training and Feedback for FDD Massive MIMO Systems

Wenqian Shen, Linglong Dai, Yi Shi, Byonghyo Shim, Zhaocheng Wang

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel-state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we first exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.

Original languageEnglish
Pages (from-to)8762-8767
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number10
Publication statusPublished - 2016 Oct

Bibliographical note

Publisher Copyright:
© 2017 IEEE.


  • Channel estimation
  • channel feedback
  • massive multiple-input multiple-output (MIMO)
  • structured sparsity
  • temporal correlation

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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