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A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels

Recently, a two-stage beamforming concept under the name of Joint Spatial Division and Multiplexing (JSDM), a kind of divide-and-conquer approach based on statistical user-grouping, has been proposed to enable simplified system operations in massive MIMO. In this study, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive MIMO systems employing single-carrier (SC) modulation in time division duplex (TDD) mode by exploiting the joint angle-delay domain channel sparsity in millimeter (mm) wave frequencies (which is often characterized with limited scattering and hence correlatedness in the spatial domain). The main contribution of this work is threefold. First, by an inspiration from the user-grouping idea (in the JSDM framework), the reduced rank minimum mean square error (RR-MMSE) instantaneous CSI estimator, based on generic subspace projection taking the joint angle-delay power profile into account, is derived for spatially correlated wideband MIMO channels. Second, the statistical prebeamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. The generalized eigenvector beamspace (GEB) appears to be a nearly optimal pre-beamformer when the eigenspaces of different resolvable multi-path components are assumed to be nearly orthogonal. Finally, a spatio-temporal domain correlator type reduced rank channel estimator, as an approximation of the RR-MMSE estimate, is obtained by carrying out least square (LS) estimation in a proper reduced dimensional beamspace. It is observed that the proposed techniques show remarkable robustness to the pilot interference (or contamination) with a significant reduction in pilot overhead thanks to the subspace projection.