Covariance Matrix Estimation in Massive MIMO
May 08, 2017 Β· Declared Dead Β· π IEEE Signal Processing Letters
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Authors
David Neumann, Michael Joham, Wolfgang Utschick
arXiv ID
1705.02895
Category
cs.IT: Information Theory
Citations
109
Venue
IEEE Signal Processing Letters
Last Checked
4 months ago
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 second order statistics of the channel vectors, e.g., to obtain 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 appropriate assignment of pilot sequences to users in consecutive channel coherence intervals.
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