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This paper considers the problem of interference control through the use of second-order statistics in massive MIMO multi-cell networks. We consider both the cases of co-located massive arrays and large-scale distributed antenna settings. We are interested in characterizing the low-rankness of users' channel covariance matrices, as such a property can be exploited towards improved channel estimation (so-called pilot decontamination) as well as interference rejection via spatial filtering. In previous work, it was shown that massive MIMO channel covariance matrices exhibit a useful finite-rank property that can be modeled via the angular spread of multipath at a MIMO uniform linear array. This paper extends this result to more general settings including certain non-uniform arrays, and more surprisingly, to two dimensional distributed large-scale arrays. In particular our model exhibits the dependence of the signal subspace's richness on the scattering radius around the user terminal, through a closed-form expression. The applications of the low-rankness covariance property to channel estimation's denoising and low-complexity interference filtering are highlighted.

Dealing With Interference in Distributed Large-Scale MIMO Systems: A Statistical Approach