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A novel approach is proposed for correlated multiple-input multiple-output (MIMO) channel estimation based on reduced-rank (RR) technique and partial channel state information (CSI). In contrast to previous proposals that used the channel correlation matrix (CCM) and its eigendecomposition, this paper shows that close linear minimum mean-square-error (LMMSE) performance can be achieved with the use of predefined bases derived from the knowledge of the maximum angular dispersion. A theoretical framework to synthesize a suitable set of bases is provided, from which discrete prolate spheroidal sequences (DPSSs) are identified as one of the appropriate predefined bases for spatial channel representation. The robustness of the proposed estimator allows changes in the propagation scenario to be managed according to the demands of realistic communications systems. The performance analysis of the channel estimator is shown and corroborated with simulation results.