Deep Learning for Multi-User MIMO Systems: Joint Design of Pilot, Limited Feedback, and Precoding

Jeonghyeon Jang, Hoon Lee, Il Min Kim, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Original languageEnglish
Pages (from-to)7279-7293
Number of pages15
JournalIEEE Transactions on Communications
Volume70
Issue number11
DOIs
Publication statusPublished - 2022 Nov 1

Keywords

  • Deep learning
  • limited feedback
  • MU-MIMO
  • precoder

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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