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Abstract

This letter presents the first work introducing a deep learning (DL) framework for channel estimation in large intelligent surface (LIS) assisted massive MIMO (multiple-input multiple-output) systems. A twin convolutional neural network (CNN) architecture is designed and it is fed with the received pilot signals to estimate both direct and cascaded channels. In a multi-user scenario, each user has access to the CNN to estimate its own channel. The performance of the proposed DL approach is evaluated and compared with state-of-the-art DL-based techniques and its superior performance is demonstrated.
Original languageEnglish
Article number9090876
Pages (from-to)1447-1451
Number of pages5
JournalIEEE Wireless Communications Letters
Volume9
Issue number9
Early online date11 May 2020
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Deep learning
  • channel estimation
  • large intelligent surfaces
  • massive MIMO

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