Deep Learning-Based Limited Feedback Designs for MIMO Systems

Jeonghyeon Jang, Hoon Lee, Sangwon Hwang, Haibao Ren, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


We study a deep learning (DL) based limited feedback methods for multi-antenna systems. Deep neural networks (DNNs) are introduced to replace an end-to-end limited feedback procedure including pilot-aided channel training process, channel codebook design, and beamforming vector selection. The DNNs are trained to yield binary feedback information as well as an efficient beamforming vector which maximizes the effective channel gain. Compared to conventional limited feedback schemes, the proposed DL method shows an 1 dB symbol error rate (SER) gain with reduced computational complexity.

Original languageEnglish
Article number8941111
Pages (from-to)558-561
Number of pages4
JournalIEEE Wireless Communications Letters
Issue number4
Publication statusPublished - 2020 Apr

Bibliographical note

Funding Information:
Manuscript received September 16, 2019; revised November 29, 2019; accepted December 17, 2019. Date of publication December 24, 2019; date of current version April 9, 2020. This work was supported in part by Huawei Technologies, and in part by National Research Foundation through the Ministry of Science, ICT, and Future Planning (MSIP), Korean Government under Grant 2017R1A2B3012316. The associate editor coordinating the review of this article and approving it for publication was S. Yang. (Corresponding author: Inkyu Lee.) Jeonghyeon Jang, Sangwon Hwang, and Inkyu Lee are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail:;;

Publisher Copyright:
© 2012 IEEE.


  • MIMO
  • deep learning
  • limited feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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