Reconfigurable intelligent surfaces (RISs) consist of many passive elements of metamaterials whose impedance can be controllable to change the characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we derive Bayesian channel estimators for two RIS-assisted massive multiple-input multiple-output (MIMO) configurations: i) the short-term RIS configuration based on the instantaneous channel estimates; ii) the long-term RIS configuration based on the channel statistics. The proposed methods exploit spatial correlation characteristics at both the base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs is developed. A computationally efficient fixed-point algorithm, which solves the max-min fairness power control optimally, is proposed. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile user equipments substantially in comparison to a conventional single-cell massive MIMO system. The impact of several channel effects are studied to gain insight about when the channel estimation, i.e., the short-term configuration, is preferable in comparison to the long-term RIS configuration to boost the spectral efficiency.

翻译：可重构智能表面（RIS）由大量可控制阻抗的超材料无源单元组成，能够改变入射无线信号的传输特性。当信道存在大量多径分量且需控制大规模RIS时，信道估计成为关键技术。本文针对两种RIS辅助大规模多输入多输出（MIMO）配置推导了贝叶斯信道估计器：i）基于瞬时信道估计的短期RIS配置；ii）基于信道统计量的长期RIS配置。所提方法利用了基站和平面RIS的空间相关性特征，以及移动环境中多镜面衰落的其他统计特性。此外，本文还开发了一种新颖的RIS相位选择启发式方法，并提出了一种计算高效的定点算法，该算法能优化求解最大最小公平功率控制问题。仿真结果表明，与传统单小区大规模MIMO系统相比，所提出的上行RIS辅助框架能显著提升小区边缘移动用户的频谱效率。通过研究多种信道效应的影响，本文深入揭示了何时应优先采用基于信道估计的短期配置而非长期RIS配置来提升频谱效率。