Reconfigurable intelligent surfaces (RISs) have attracted great attention as a potential beyond 5G technology. These surfaces 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 propose novel channel estimation schemes for different RIS-assisted massive multiple-input multiple-output (MIMO) configurations. 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. For the RIS-assisted massive MIMO, a new receive combining method and a fixed-point algorithm, which solves the max-min fairness power control optimally, are 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 which RIS configuration is preferable and when the channel estimation is necessary to boost the spectral efficiency.
翻译:在5G技术之外,重新配置的智能表面(RIS)作为潜在的5G技术,吸引了极大的关注。这些表面包括许多被动的元材料元素,这些元材料的阻力可以控制,以改变无线信号的特性。当拥有一个拥有大量多路组件的频道时,频道估计对于控制大型的RIS来说是一项关键任务。在本文件中,我们建议为不同的RIS辅助的大规模多投入的多输出(MIMO)配置制定新的频道估计计划。拟议的系统利用基站和平面RIS的空间相关特征,以及移动环境中多光谱淡化的其他统计特征。此外,对于在RIS中进行阶段转移选择,正在开发一种新型的超繁忙性,对于拥有大量多路段组件的频道。我们提出了一种新的组合方法和固定点算法,它能最佳地解决最大度的公平性电源控制。模拟结果显示,拟议的RIS辅助框架将提高基站站和平面RIS的光谱效率,以及移动式用户设备对常规光谱系统的影响,在对几条高端的光谱分析中,将获得对高端系统影响进行一次的深度分析。