Statistical Beamforming Based on Effective Channel Gain for Spatially Correlated Massive MIMO Systems

Youngrok Jang, Taehyoung Kim, Kyungsik Min, Minchae Jung, Sooyong Choi

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


A statistical beamforming (SBF) scheme based on the effective channel gain (ECG) is proposed for spatially correlated massive multiple-input multiple-output systems. The proposed SBF scheme consists of outer and inner precoders. The outer precoder is designed to eliminate multiuser interference based on the correlation matrix of downlink channel. Then, according to the ECG defined as the squared inner product of the dedicated channel and an arbitrary precoding vector, the inner precoder is selected among the eigenvectors of the dedicated channel's correlation matrix with the maximum ECG. Simulation results show that the proposed SBF scheme outperforms conventional schemes.

Original languageEnglish
Article number8012359
Pages (from-to)197-200
Number of pages4
JournalIEEE Communications Letters
Issue number1
Publication statusPublished - 2018 Jan

Bibliographical note

Funding Information:
Manuscript received July 27, 2017; accepted August 12, 2017. Date of publication August 17, 2017; date of current version January 8, 2018. This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00181, Development on the core technologies of transmission, modulation and coding with low-power and low-complexity for massive connectivity in the IoT environment) and DMC R&D Center, Samsung Electronics Company, Ltd., under Grant IO 150605-03060-01. The associate editor coordinating the review of this paper and approving it for publication was F. Gao. (Corresponding author: Sooyong Choi.) Y. Jang, K. Min, M. Jung, and S. Choi are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail:

Publisher Copyright:
© 1997-2012 IEEE.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


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