Hybrid Precoding Revisited: Low-Dimensional Subspace Perspective for MU-MIMO Systems

TL;DR

This paper introduces a low-complexity hybrid precoding framework for MU-MIMO systems that leverages low-dimensional subspace properties, optimizing performance and complexity, and extending to adaptive antenna partitioning and partial CSIT scenarios.

Contribution

It proposes a novel low-dimensional subspace-based hybrid precoding framework with adaptive antenna partitioning and partial CSIT exploitation, reducing complexity and enhancing performance.

Findings

Achieves superior performance compared to existing methods.

Significantly reduces computational complexity.

Robustness with partial channel state information.

Abstract

This letter presents a low-complexity hybrid precoding framework for multiuser multiple-input multiple-output (MIMO) systems by leveraging a low-dimensional subspace property. Under the low-dimensional subspace perspective, we first identify an unconstrained optimal radio-frequency (RF) precoder. We then optimize a hybrid precoder via a reduced-complexity precoding method. We further extend the proposed framework to (i) a dynamic-subarray antenna partitioning algorithm that adaptively allocates subsets of antennas associated with RF chains, and (ii) a channel covariance-based approach to exploit statistical channel state information at a transmitter (CSIT), ensuring robustness with partial CSIT. Simulations validate that our proposed algorithms achieve superior performance while significantly reducing complexity compared to existing methods.