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

2 Citations (Scopus)

Abstract

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
Volume9
Issue number4
DOIs
Publication statusPublished - 2020 Apr

Keywords

  • MIMO
  • deep learning
  • limited feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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