Impact of Strong Spatial Correlation on the Capacity Scaling of Massive MIMO

In this paper, the capacity scaling of multicell massive MIMO systems is investigated in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the "random sparse angular support" model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. In order to achieve the capacity results, spatial (de)spreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users per cell scales linearly with base station antennas in multicell environments, unlimited capacity is achievable under the sparse angular support model as long as the effective signal-to-noise ratio is away from zero.