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Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and user grouping. Considering recently proposed massive MIMO systems, covariance estimation proves to be challenging due to the large number of antennas (M >> 1) employed in the base station. In this case, the number of pilot transmissions N becomes comparable to the number of antennas and standard estimators, such as the sample covariance, yield a poor estimate of the true covariance and are hence undesirable. In this paper, we propose a Maximum-Likelihood (ML) massive MIMO covariance estimator, based on a parametric representation of the channel angular spread function (ASF). The parametric representation emerges from super-resolving discrete ASF components plus approximating its continuous components using …