Covariance Matrix Estimation in Massive MIMO

David Neumann, Michael Joham, Wolfgang Utschick

Research output: Contribution to journalArticlepeer-review

83 Scopus citations

Abstract

Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by the appropriate assignment of pilot sequences to the users in consecutive channel coherence intervals. To keep the computational complexity in check, we exploit common structure of the covariance matrices.

Original languageEnglish
Pages (from-to)863-867
Number of pages5
JournalIEEE Signal Processing Letters
Volume25
Issue number6
DOIs
StatePublished - Jun 2018

Keywords

  • Covariance matrix estimation
  • massive MIMO
  • pilot allocation
  • pilot-contamination

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