ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections

Dong Jin Ji, Jinsol Park, Dong Ho Cho

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers. This configuration increases performance while decreasing computational complexity at run-Time compared with conventional channel autoencoders. The simulations using both conventional and proposed autoencoders for a 2-by-2 multiple-input multiple-output (MIMO) system under Rayleigh and Nakagami-m fading show that the ConvAE is able to attain a lower bit error rate and higher achievable rate relative to the conventional channel autoencoder schemes.

Original languageEnglish
Article number8768327
Pages (from-to)1769-1772
Number of pages4
JournalIEEE Communications Letters
Volume23
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

  • Autoencoder
  • convolutional neural network
  • deep learning
  • multiple input multiple output

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