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 phase, amplitude, or other 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 a novel channel estimation scheme that exploits spatial correlation characteristics at both the massive multiple-input multiple-output (MIMO) 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, inspired by signal processing methods that are effective in conventional massive MIMO. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile users substantially in comparison to a conventional single-cell massive MIMO system.
翻译:重新配置的智能表面(RIS)作为5G技术以外的一种潜力吸引了极大的注意。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,以改变干扰这些表面的无线信号的阶段、振幅或其他特性。频道估计对于大型RIS的控制来说是一项关键任务,因为它拥有一个拥有大量多路部件的频道。在本文件中,我们提议了一个新型的频道估计计划,利用大规模多投入多输出(MIMO)基站和规划性RIS的空间相关特征,以及移动环境中多孔径变化的其他统计特征。此外,在常规大型IMO有效的信号处理方法的启发下,为RIS的阶段性选择开发了新颖的动力。模拟结果表明,拟议的上链-RIS辅助框架与常规的单细胞大型IMO系统相比,大大提高了细胞端移动用户的光谱效率。