Deep Learning-based Limited Feedback Designs for MIMO Systems

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

Research output: Contribution to journalArticle

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
JournalIEEE Wireless Communications Letters
DOIs
Publication statusAccepted/In press - 2019 Jan 1

Keywords

  • deep learning
  • limited feedback.
  • MIMO

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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