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This paper investigates the capacity scaling of multicell massive MIMOsystems in the presence of spatially correlated fading. In particular, we focuson the strong spatial correlation regimes where the covariance matrix of eachuser channel vector has a rank that scales sublinearly with the number of basestation antennas, as the latter grows to infinity. We also consider the casewhere the covariance eigenvectors corresponding to the non-zero eigenvaluesspan randomly selected subspaces. For this channel model, referred to as the"random sparse angular support" model, we characterize the asymptotic capacityscaling law in the limit of large number of antennas. To achieve the asymptoticcapacity results, statistical spatial despreading based on the second-orderchannel statistics plays a pivotal role in terms of pilot decontamination andinterference suppression. A remarkable result is that even when the number ofusers scales linearly with base station antennas, a linear growth of thecapacity with respect to the number of antennas is achievable under the sparseangular support model. We note that the achievable rate lower bound based onmassive MIMO "channel hardening", widely used in the massive MIMO literature,yields rather loose results in the strong spatial correlation regimes and maysignificantly underestimate the achievable rate of massive MIMO. This worktherefore considers an alternative bounding technique which is better suited tothe strong correlation regimes. In fading channels with sparse angular support,it is further shown that spatial despreading (spreading) in uplink (downlink)has a more prominent impact on the performance of massive MIMO than channelhardening.

Capacity Scaling of Massive MIMO in Strong Spatial Correlation Regimes