论文标题
利用阵列几何形状用于降低RIS辅助通信中的降低空间通道估计
Exploiting Array Geometry for Reduced-Subspace Channel Estimation in RIS-Aided Communications
论文作者
论文摘要
可重新配置的智能表面（RIS）可用于改善基站（BS）和用户设备（UE）之间的通道增益，但前提是将其反射元素的$ n $配置正确。这需要通过每个RIS元素准确地估算从UE到BS的级联通道。如果未利用通道结构，则必须使用长度$ n $的试点序列，这是一个主要的实际挑战，因为$ n $通常按数百个订单。为了解决此问题而不需要用户特定的通道统计信息，我们提出了一个新颖的估计器，称为“降低 - 空间最小二乘正方形（RS-LS）估计器”，该估计器仅使用数组的知识。优化了RIS相移模式，以最大程度地减少通道估计的均方误差。 RS-LS估计器在很大程度上胜过常规的最小二乘估计器，并且可以使用较短的飞行员长度使用，因为它利用了一个事实，即阵列几何形状将可能的通道实现限制在降低额度子空间中。
A reconfigurable intelligent surface (RIS) can be used to improve the channel gain between a base station (BS) and user equipment (UE), but only if its $N$ reflecting elements are configured properly. This requires accurate estimation of the cascaded channel from the UE to the BS through each RIS element. If the channel structure is not exploited, pilot sequences of length $N$ must be used, which is a major practical challenge since $N$ is typically at the order of hundreds. To address this problem without requiring user-specific channel statistics, we propose a novel estimator, called reduced-subspace least squares (RS-LS) estimator, that only uses knowledge of the array geometry. The RIS phase-shift pattern is optimized to minimize the mean-square error of the channel estimates. The RS-LS estimator largely outperforms the conventional least-squares estimator, and can be utilized with a much shorter pilot length since it exploits the fact that the array geometry confines the possible channel realizations to a reduced-rank subspace.
