Robust Statistical Beamforming with Multi-Cluster Tracking for Time-Varying Massive MIMO (Extended Version)
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In this paper, a joint design of instantaneous channel estimation, beam tracking, and adaptive beamformer construction for a massive multiple-input multiple-output (MIMO) system is proposed. This design focuses on efficiency in terms of performance and computational complexity under the adverse effects of time variation and mobility of sources, the presence of multiuser and multipath components, or simply multi-clusters, and the near-far effect. The design is also suitable for hybrid beamforming and frequency-selective channels. In the proposed system, channel parameters are estimated in time-domain duplex (TDD) uplink mode using a per-cluster approach rather than a joint approach, which significantly reduces the complexity. Per-cluster estimation is possible thanks to the proposed interference-aware statistical beamforming method, namely reduced dimensional Generalized Eigenbeamformer (RD-GEB), which undertakes the computational load of interference mitigation and enables a simpler design for the remaining stages. In addition, the overall design is based on the separation of channel parameters as fast-time and slow-time, leaving only the instantaneous channel estimation and channel matched filtering as fast-time operations, which are handled inside cluster-specific reduced dimensional subspaces. Beam tracking and beamformer construction are held in slow-time rarely, which reduces the time-averaged complexity. Furthermore, beam tracking is performed by leveraging a batch of instantaneous channel estimates, which removes the need for an additional training process. The proposed low-complexity design is shown to outperform the conventional methods.
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