High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches

Juei Chin Shen, Jun Zhang, Kwang Cheng Chen, Khaled Letaief

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Massive multiple-input-multiple-output (MIMO) has been regarded as one of the key technologies for fifth-generation wireless networks, as it can significantly improve both the spectral efficiency and the energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design tradeoffs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified.

Original languageEnglish
Article number7756364
Pages (from-to)32-40
Number of pages9
JournalIEEE Systems Journal
Volume11
Issue number1
DOIs
Publication statusPublished - 1 Mar 2017
Externally publishedYes

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Keywords

  • Channel estimation
  • compressed sensing
  • massive multiple-input-multiple-output (MIMO)
  • pilot contamination
  • pilot sequences
  • sparsity
  • ℓ minimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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