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Hybrid beamforming (HB) has been widely studied for reducing the number ofcostly radio frequency (RF) chains in massive multiple-input multiple-output(MIMO) systems. However, previous works on HB are limited to a single userequipment (UE) or a single group of UEs, employing the frequency-flatfirst-level analog beamforming (AB) that cannot be applied to multiple groupsof UEs served in different frequency resources in an orthogonalfrequency-division multiplexing (OFDM) system. In this paper, a novel HBalgorithm with unified AB based on the spatial covariance matrix (SCM)knowledge of all UEs is proposed for a massive MIMO-OFDM system in order tosupport multiple groups of UEs. The proposed HB method with a much smallernumber of RF chains can achieve more than 95% performance of full digitalbeamforming. In addition, a novel practical subspace construction (SC)algorithm based on partial channel state information is proposed to estimatethe required SCM. The proposed SC method can offer more than 97% performance ofthe perfect SCM case. With the proposed methods, significant cost and powersavings can be achieved without large loss in performance. Furthermore, theproposed methods can be applied to massive MIMO-OFDM systems in bothtime-division duplex and frequency-division duplex.

A Novel Hybrid Beamforming Algorithm with Unified Analog Beamforming by Subspace Construction Based on Partial CSI for Massive MIMO-OFDM Systems