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Uplink channel estimation is a crucial component for the performance ofcellular massive MIMO systems. However, when the number of user equipments(UEs) grows, the sharing of the available resources causes interference betweenUEs in neighboring cells. Minimum mean squared error (MMSE) channel estimatorshave been proposed to mitigate this interference, but these require estimatesof the channel covariance matrices. Therefore, a new channel covariance matrixestimator for low-rank channel covariance matrices is presented in this paper,using a generalized eigenvalue decomposition (GEVD) of two covariance matricesthat can be estimated from the available uplink data. The requirements for thesystems are minimal and, except for synchronization, there is no need forcommunication between the different cells and no prior knowledge on thebackground noise is required. Approximate MMSE estimators are also derivedbased on the newly proposed channel covariance matrix estimator. Theeffectiveness of the proposed methods is demonstrated in numerical simulations.

GEVD-based Low-Rank Channel Covariance Matrix Estimation and MMSE Channel Estimation for Uplink Cellular Massive MIMO Systems