Multi-user linear precoding for multi-polarized Massive MIMO system under imperfect CSIT

Jaehyun Park, Bruno Clercks

Research output: Contribution to journalArticle

44 Citations (Scopus)

Abstract

The space limitation and channel acquisition prevent Massive MIMO from being easily deployed in a practical setup. Motivated by current deployments of LTE-Advanced, the use of multi-polarized antenna elements can be an efficient solution to address the space constraint. Furthermore, the dual-structured precoding, in which a preprocessing based on the spatial correlation and a subsequent linear precoding based on the short-term channel state information at the transmitter (CSIT) are concatenated, can reduce the feedback overhead efficiently. By grouping and preprocessing spatially correlated mobile stations (MSs), the dimension of the precoding signal space is reduced and the corresponding short-term CSIT dimension is reduced. In this paper, to reduce the feedback overhead further, we propose a dual-structured multi-user linear precoding, in which the subgrouping method based on co-polarization is additionally applied to the spatially grouped MSs in the preprocessing stage. Furthermore, under imperfect CSIT, the proposed scheme is asymptotically analyzed based on random matrix theory. By investigating the behavior of the asymptotic performance, we also propose a new dual-structured precoding in which the precoding mode is switched between two dual-structured precoding strategies with 1) the preprocessing based only on the spatial correlation and 2) the preprocessing based on both the spatial correlation and polarization. Finally, we extend it to 3D dual-structured precoding.

Original languageEnglish
Article number7001705
Pages (from-to)2532-2547
Number of pages16
JournalIEEE Transactions on Wireless Communications
Volume14
Issue number5
DOIs
Publication statusPublished - 2015 May 1

Keywords

  • Dual structured precoding with long-term/short-term CSIT
  • Multi-polarized Massive MIMO

ASJC Scopus subject areas

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

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