Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

中文翻译：

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具有可重构智能表面的信道估计——一个通用框架

最佳地提取无线通信系统中可重构智能表面 (RIS) 的优势需要估计进出 RIS 的通道。当 RIS 由没有任何传感或数据处理能力的无源元件组成时，确定这些信道的过程很复杂，因此，必须通过非共置设备（通常是控制基站 (BS)）间接估计信道。在本文中，我们从基本的角度研究了基于无源 RIS 系统的信道估计。我们研究了各种可能的通道模型和模型的可识别性，作为可用的试验数据和训练期间 RIS 的行为的函数。特别是，我们将考虑有和没有视线传播的情况，用户和 BS 的单天线和多天线配置、相关和稀疏信道模型、单载波和宽带正交频分复用 (OFDM) 场景、用户和 BS 之间直接链路的可用性、先验信息的利用，以及其他一些特殊情况。我们使用相关的 Cramér-Rao 界限进一步对代表性算法进行模拟，并比较它们对各种通道模型的性能。