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Real-Time MU-MIMO Beamforming with Limited Channel Samples in 5G Networks

  • Shaoran Li
  • , Nan Jiang
  • , Chengzhang Li
  • , Shiva Acharya
  • , Yubo Wu
  • , Weijun Xie
  • , Wenjing Lou
  • , Y. Thomas Hou*
  • *Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

MU-MIMO beamforming is a key technology for 5G networks, relying on Channel State Information (CSI). However, in practice, the estimated CSI in reality is prone to uncertainty. Further, a MU-MIMO beamforming solution must be derived within a millisecond to be useful for real-time 5G applications. We present ReDBeam - a real-time data-driven beamforming solution for MU-MIMO using limited CSI data samples. The main novelties of ReDBeam are a parallel algorithm and an optimized GPU implementation. ReDBeam delivers a MU-MIMO beamforming solution within 1 millisecond to meet the probabilistic data rate requirements from the users, and minimize a base station's power consumption. Through extensive experiments, we show that ReDBeam consistently meets the stringent 1-millisecond real-time requirement and is orders of magnitude faster than other state-of-the-art algorithms. ReDBeam conclusively demonstrates that MU-MIMO beamforming with data rate requirements can be achieved in real-time using only limited CSI data samples. © 2002-2012 IEEE.
Original languageEnglish
Pages (from-to)13549-13566
Number of pages18
JournalIEEE Transactions on Mobile Computing
Volume24
Issue number12
Online published25 Jul 2025
DOIs
Publication statusPublished - Dec 2025
Externally publishedYes

Funding

This work was supported in part by NSF under Grant CNS-2312447 and Grant CMMI-2246414, in part by ONR under MURI under Grant N00014-19-1-2621, and in part by Commonwealth Cyber Initiative (CCI).

Research Keywords

  • 5G
  • beamforming
  • Channel uncertainty
  • GPU
  • limited data samples
  • MU-MIMO
  • real-time

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