Compressed CSI Acquisition in FDD Massive MIMO: How Much Training is Needed?

J.-C. Shen, Jun Zhang, E. Alsusa, K.B. Letaief

Research output: Journal article publicationJournal articleAcademic researchpeer-review

78 Citations (Scopus)


© 2016 IEEE.Massive multiple-input-multiple-output (MIMO) is a promising technique for providing unprecedented spectral efficiency. However, it has been well recognized that the excessive training overhead required for obtaining the channel side information is a major handicap in frequency-division duplexing (FDD) massive MIMO. Several attempts have been made to reduce this training overhead by exploiting the sparsity structures of massive MIMO channels. So far, however, there has been little discussion about how to exploit the partial support information of these channels to achieve further overhead reductions. Such information, which is a set of indices of the significant elements of a channel vector, can be acquired in advance and hence is an important option to explore. In this paper, we examine the impact on the required training overhead when this information is applied within a weighted l1 minimization framework, and analytically show that a sharp estimate of the reduced overhead size can be successfully obtained. Furthermore, we examine how the accuracy of the partial support information impacts the achievable overhead reduction. Numerical results for a wide range of sparsity and partial support information reliability levels are presented to quantify our findings and main conclusions.
Original languageEnglish
Article number7420744
Pages (from-to)4145-4156
Number of pages12
JournalIEEE Transactions on Wireless Communications
Issue number6
Publication statusPublished - 1 Jun 2016
Externally publishedYes


  • channel estimation
  • compressed sensing
  • FDD
  • Massive MIMO
  • partial support information
  • phase transition
  • pilot contamination
  • weighted l1 minimization

ASJC Scopus subject areas

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


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