QRD-Based Precoding Design for Multiuser Massive MIMO Null-Space Expansion

This article proposes a precoding weight suitable for null-space expansion (NSE) in multi-user massive multiple-input multiple-output (MU-mMIMO). NSE improves inter-user interference (IUI) suppression performance by steering multiple nulls for interfering user terminals (UTs). However, NSE faces challenges in effectively forming beams and nulls for IUI suppression, as well as in managing the computational complexity for precoding. We employ QR decomposition-based Gram-Schmidt orthogonalization (QRGSO) as a precoding algorithm suitable for NSE. Computer simulation demonstrates the superiority of the proposed scheme in terms of IUI suppression performance and computation complexity.

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Publication Details

Journal
IEICE Communications Express
Published
2024-10-10
DOI
https://doi.org/10.23919/comex.2024xbl0150
Citations
6
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
1.25
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article

QRD-Based Precoding Design for Multiuser Massive MIMO Null-Space Expansion

Kazuki Maruta, Yuki Sasaki, Kabuto Arai
6 citations
IEICE Communications Express
Advanced MIMO Systems Optimization
1.25
article

QRD-Based Precoding Design for Multiuser Massive MIMO Null-Space Expansion

Kazuki Maruta, Yuki Sasaki, Kabuto Arai
article en
6 citations

Abstract

This article proposes a precoding weight suitable for null-space expansion (NSE) in multi-user massive multiple-input multiple-output (MU-mMIMO). NSE improves inter-user interference (IUI) suppression performance by steering multiple nulls for interfering user terminals (UTs). However, NSE faces challenges in effectively forming beams and nulls for IUI suppression, as well as in managing the computational complexity for precoding. We employ QR decomposition-based Gram-Schmidt orthogonalization (QRGSO) as a precoding algorithm suitable for NSE. Computer simulation demonstrates the superiority of the proposed scheme in terms of IUI suppression performance and computation complexity.

IEICE Communications ExpressVol. 13(12)
TEPCO (Japan) (JP), Tokyo University of Science (JP), University of Electro-Communications (JP)
Openalex Percentile: Top 20%
Advanced MIMO Systems Optimization
1.25
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