Deep learning-based antenna selection and CSI extrapolation in massive MIMO systems

Bo Lin, Feifei Gao, Shun Zhang, Ting Zhou, Ahmed Alkhateeb

Research output: Contribution to journalArticlepeer-review

Abstract

A critical bottleneck of massive multiple-input multiple-output (MIMO) system is the huge training overhead caused by downlink transmission, like channel estimation, downlink beamforming and covariance observation. In this paper, we propose to use the channel state information (CSI) of a small number of antennas to extrapolate the CSI of the other antennas and reduce the training overhead. Specifically, we design a deep neural network that we call an antenna domain extrapolation network (ADEN) that can exploit the correlation function among antennas. We then propose a deep learning (DL) based antenna selection network (ASN) that can select a limited antennas for optimizing the extrapolation, which is conventionally a type of combinatorial optimization and is difficult to solve. We trickly designed a constrained degradation algorithm to generate a differentiable approximation of the discrete antenna selection vector such that the back-propagation of the neural network can be guaranteed. Numerical results show that the proposed ADEN outperforms the traditional fully connected one, and the antenna selection scheme learned by ASN is much better than the trivially used uniform selection.

Original languageEnglish (US)
Pages (from-to)7669-7681
Number of pages13
JournalIEEE Transactions on Wireless Communications
Volume20
Issue number11
DOIs
StatePublished - Nov 1 2021

Keywords

  • antenna selection
  • beam prediction
  • channel covariance matrix
  • Channel extrapolation
  • deep learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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