Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO

Tian Xie, Linglong Dai, Xinyu Gao, Muhammad Zeeshan Shakir, Jianjun Li

Research output: Contribution to journalArticle

Abstract

Hybrid precoding can reduce the number of required radio frequency (RF) chains in millimeter-Wave (mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition (SVD) requires the complicated bit allocation to match the different signal-tonoise-ratios (SNRs) of different sub-channels. In this paper, we propose a geometric mean decomposition (GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically, we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit (OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMDbased hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight
increase in complexity.
Original languageEnglish
Pages (from-to)229-238
Number of pages10
JournalChina Communications
Volume15
Issue number5
DOIs
Publication statusPublished - 19 Jun 2018

Fingerprint

Singular value decomposition
MIMO systems
Millimeter waves

Keywords

  • Precoding
  • MIMO communication
  • Bit rate
  • Matching pursuit algorithms
  • Radio frequency
  • Millimeter wave technology
  • Complexity theory

Cite this

Xie, Tian ; Dai, Linglong ; Gao, Xinyu ; Shakir, Muhammad Zeeshan ; Li, Jianjun . / Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO. In: China Communications. 2018 ; Vol. 15, No. 5. pp. 229-238.
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Geometric mean decomposition based hybrid precoding for millimeter-wave massive MIMO. / Xie, Tian; Dai, Linglong ; Gao, Xinyu; Shakir, Muhammad Zeeshan; Li, Jianjun .

In: China Communications, Vol. 15, No. 5, 19.06.2018, p. 229-238.

Research output: Contribution to journalArticle

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