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

TL;DR

This paper proposes a comprehensive framework for CSI estimation and pre-beamformer design in single-carrier massive MIMO systems operating in millimeter wave channels, leveraging joint angle-delay domain sparsity for improved performance.

Contribution

It introduces a generic subspace projection and reduced rank MMSE estimator tailored for frequency-selective mmWave MIMO channels, enhancing robustness and reducing pilot overhead.

Findings

Robustness to pilot contamination demonstrated

Significant reduction in pilot overhead achieved

Accurate channel estimation via reduced rank methods

Abstract

In this paper, we establish a general framework on the reduced dimensional channel state information (CSI) estimation and pre-beamformer design for frequency-selective massive multiple-input multiple-output 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. First, based on a generic subspace projection taking the joint angle-delay power profile and user-grouping into account, the reduced rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived for spatially correlated wideband MIMO channels. Second, the statistical pre-beamformer design is considered for frequency-selective SC massive MIMO channels. We examine the dimension reduction problem and subspace (beamspace) construction on which the RR-MMSE estimation can be realized as accurately as possible. 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.