arrow
Return

Gyre Precoding for MU-MIMO Systems

delete2021-08-01
delete4
delete
OA
AI
J
J. C. De Luna Ducoing
Y
Yun Xue
K
Konstantinos Nikitopoulos *
DOI:10.1109/LCOMM.2021.3082808delete
deleteOriginal
deleteShare
deleteSave
View PDF
Abstract

Abstract

En 中文
This work introduces Gyre Precoding (GP), a novel linear multi-user multiple-input multiple-output (MU-MIMO) precoding approach. GP performs rotations of the symbols of each spatial layer to optimize the precoding performance. To find the rotation angles, we propose a nearoptimal, gradient descent-based low-complexity algorithm. GP is constellation-agnostic and does not require significant changes to conventional receiver procedures or wireless standards. Computer evaluation results show that GP can achieve 8 dB SNR gains over linear precoding techniques and 2 dB over suboptimal symbol-level precoding (SLP) methods for a 16 x 16 MU-MIMO system. Furthermore, in a 64 x 12 massive-MIMO scenario in a 5G New Radio (5GNR) setup, GP achieves a 13% higher throughput gain over zero-forcing precoding.
Keywords:
Multi-user multiple-input multiple-output (MU-MIMO)
precoding
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

IEEE Communications Letters cover
IEEE Communications Letters
IF:
4.4
Papers:
1.3W
Citations:
2.2W

Organization

H
huawei technologies
Scholars:
3.3K
Papers: 2.9K
Citations: 1
U
University of Surrey
Scholars:
1.2W
Papers: 1.3W
Citations: 22
Cited Papers

Cited Papers

Power Minimizer Symbol-Level Precoding: A Closed-Form Suboptimal Solution
err2018-01-01
err27
errOAAI
errHaqiqatnejad, Alireza; Kayhan, Farbod; Ottersten, Bjorn
errShare
errSave
no more