A hybrid approach for multi-user massive MIMO sparse channel estimation based on Bayesian recovery and hard thresholding

Javier Garcia, Jawad Munir, Amine Mezghani, Josef A. Nossek

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We propose an efficient sparse channel estimation algorithm based on the compressed sensing (CS) approach for large scale multi-user (MU) MIMO systems. The proposed scheme is a hybrid one comprising Bayesian and greedy methods. It can improve the estimation performance by incorporating the spatial channel knowledge that the neighboring antennas in an array share the same support. The pilot overhead can be reduced by utilizing the data symbols using a reliability measure for channel estimation. Moreover, the effect of interfering and non-interfering pilots on the estimation performance will be investigated. It will be shown that the proposed hybrid technique performs similar or better than the Bayesian method with substantially reduced complexity.

Original languageEnglish
Title of host publication2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467395267
DOIs
StatePublished - 2015
EventIEEE Globecom Workshops, GC Wkshps 2015 - San Diego, United States
Duration: 6 Dec 201510 Dec 2015

Publication series

Name2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings

Conference

ConferenceIEEE Globecom Workshops, GC Wkshps 2015
Country/TerritoryUnited States
CitySan Diego
Period6/12/1510/12/15

Keywords

  • Compressed sensing
  • Distributed channel estimation
  • Multi-user massive MIMO
  • OFDM
  • Sparse recovery

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