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In this paper, a joint design of instantaneous channel estimation, beamtracking, and adaptive beamformer construction for a massive multiple-inputmultiple-output (MIMO) system is proposed. This design focuses on efficiency interms of performance and computational complexity under the adverse effects oftime variation and mobility of sources, the presence of multiuser and multipathcomponents, or simply multi-clusters, and the near-far effect. The design isalso suitable for hybrid beamforming and frequency-selective channels. In theproposed system, channel parameters are estimated in time-domain duplex (TDD)uplink mode using a per-cluster approach rather than a joint approach, whichsignificantly reduces the complexity. Per-cluster estimation is possible thanksto the proposed interference-aware statistical beamforming method, namelyreduced dimensional Generalized Eigenbeamformer (RD-GEB), which undertakes thecomputational load of interference mitigation and enables a simpler design forthe remaining stages. In addition, the overall design is based on theseparation of channel parameters as fast-time and slow-time, leaving only theinstantaneous channel estimation and channel matched filtering as fast-timeoperations, which are handled inside cluster-specific reduced dimensionalsubspaces. Beam tracking and beamformer construction are held in slow-timerarely, which reduces the time-averaged complexity. Furthermore, beam trackingis performed by leveraging a batch of instantaneous channel estimates, whichremoves the need for an additional training process. The proposedlow-complexity design is shown to outperform the conventional methods.

Robust Statistical Beamforming with Multi-Cluster Tracking for Time-Varying Massive MIMO (Extended Version)