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

33 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 frequencydivision 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 firstly 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. (c) 2015 IEEE.

Original languageEnglish
Article number7353214
JournalIEEE Transactions on Vehicular Technology
Issue number99
Publication statusPublished - 2015
Externally publishedYes


  • Channel estimation
  • Channel feedback
  • Massive MIMO
  • Structured sparsity
  • Temporal correlation

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Computer Networks and Communications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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