Enhanced sparse Bayesian learning-based channel estimation for massive MIMO-OFDM systems

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Abstract

Pilot contamination limits the potential benefits of massive multiple input multiple output (MIMO) systems. To mitigate pilot contamination, in this paper, an efficient channel estimation approach is proposed for massive MIMO systems, using sparse Bayesian learning (SBL) namely coupled hierarchical Gaussian framework where the sparsity of each coefficient is controlled by its own hyperparameter and the hyperparameters of its immediate neighbours. The simulation results show that the proposed method can reconstruct original channel coefficients more effectively compared to the conventional channel estimators in terms of channel estimation accuracy in the presence of pilot contamination.

Original languageEnglish
Title of host publication2017 European Conference on Networks and Communications (EuCNC)
Subtitle of host publicationEuCNC 2017 Travel Grant Support
Place of PublicationOulu, Finland
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538638736
DOIs
Publication statusPublished - 15 Jun 2017
Event2017 European Conference on Networks and Communications, EuCNC 2017 - Oulu, Finland
Duration: 12 Jun 201715 Jun 2017

Conference

Conference2017 European Conference on Networks and Communications, EuCNC 2017
Country/TerritoryFinland
CityOulu
Period12/06/201715/06/2017

Keywords

  • channel estimation
  • massive MIMO
  • Sparse Bayesian learning

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