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.

翻译：本文提出了一种面向大规模多输入多输出（MIMO）系统的联合设计方案，该方案整合了瞬时信道估计、波束跟踪与自适应波束成形器构建。该设计旨在应对时变特性与信源移动性、多用户与多径分量（即多簇）共存以及远近效应等不利条件下的性能与计算复杂度权衡问题。该方案同时适用于混合波束成形与频率选择性信道。在所提系统中，信道参数通过时域双工（TDD）上行链路采用逐簇而非联合估计方法进行估计，从而显著降低复杂度。逐簇估计的实现得益于所提出的干扰感知统计波束成形方法——即降维广义特征波束成形器（RD-GEB），该方法承担了干扰抑制的计算负载，并简化了后续模块的设计。此外，整体设计基于信道参数在快时间与慢时间上的分离，仅将瞬时信道估计与信道匹配滤波作为快时间操作，这些操作在簇特定的降维子空间内处理。波束跟踪与波束成形器构建在慢时间域低频执行，从而降低了时均复杂度。同时，波束跟踪利用历史瞬时信道估计的批次数据实现，无需额外训练过程。仿真结果表明，所提出的低复杂度设计方案在性能上优于传统方法。