Estimating MIMO channel covariances from training data under the Kronecker model

  • Authors:
  • Karl Werner;Magnus Jansson

  • Affiliations:
  • Department of Electrical Engineering, ACCESS Linnaeus Center, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden;Department of Electrical Engineering, ACCESS Linnaeus Center, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden

  • Venue:
  • Signal Processing
  • Year:
  • 2009

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Abstract

Many algorithms for transmission in multiple input multiple output (MIMO) communication systems rely on second order statistics of the channel realizations. The problem of estimating such second order statistics of MIMO channels, based on limited amounts of training data, is treated in this article. It is assumed that the Kronecker model holds. This implies that the channel covariance is the Kronecker product of one covariance matrix that is associated with the array and the scattering at the transmitter and one that is associated with the receive array and the scattering at the receiver. The proposed estimator uses training data from a number of signal blocks (received during independent fades of the MIMO channel) to compute the estimate. This is in contrast to methods that assume that the channel realizations are directly available, or possible to estimate almost without error. It is also demonstrated how methods that make use of the training data indirectly via channel estimates can be biased. An estimator is derived that can, in an asymptotically optimal way, use, not only the structure implied by the Kronecker assumption, but also linear structure on the transmit- and receive covariance matrices. The performance of the proposed estimator is analyzed and numerical simulations illustrate the results and also provide insight into the small sample behaviour of the proposed method.