Steerable Subarrays for Practical mmWave Massive MIMO: Algorithm Design and System-Level Analysis

Abstract

This paper investigates the application of recently proposed practical subarray (SA)-based hybrid beamforming (HBF) architectures—implemented entirely with passive beamforming networks and switches—for millimeter wave (mmWave) multi-user (MU)-MIMO base stations. Building on this practical hardware platform, we propose a joint SA configuration and signal processing framework that exploits the natural non-uniformity of user locations in 3-D space via elevation domain subsectorization. Specifically, we adapt established channel estimation and HBF techniques to the constraints of switch-based SAs, and introduce a novel 2-stage channel estimator that leverages the unique properties of mmWave channels. System-level simulations in realistic line-of-sight (LoS) and non-line-of-sight (NLoS) propagation scenarios demonstrate that the proposed solution delivers strong performance with low complexity, providing a viable path toward practical, scalable mmWave MU-MIMO deployments. In LoS scenarios, using directions-of-arrival-based channel estimation, the proposed framework achieves up to 92.6% of the average spectral efficiency (SE) of a full-digital array antenna with the same number of elements but 4 times more radio frequency chains. In NLoS environments, using the novel 2-stage estimator, this increases up to 99.7%.