Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems
Channel Training & Estimation for Reconfigurable Intelligent Surfaces: Exposition of Principles, Approaches, and Open Problems
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DOI：
10.1109/access.2023.3236909
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发表时间：
2023-01
期刊：
影响因子：
3.9
通讯作者：
Bharath Shamasundar;Negar Daryanavardan;Aria Nosratinia
中科院分区：
文献类型：
--
作者：
Bharath Shamasundar;Negar Daryanavardan;Aria Nosratinia
可重构智能表面（RIS）是小型反射器的无源可控阵列，其将电磁能量引导朝向或远离目标节点，从而允许更好地管理无线网络中的信号和干扰。RIS具有显著提高无线网络性能的潜力。不幸的是，RIS还增加了发射机和接收机之间的信道状态信息（CSI）系数的数量，这放大了估计和传送信道状态信息的挑战。此外，被动RIS的简单性和成本效益还意味着不在RIS处本地估计输入链路，并且不将新的导频插入到输出RIS链路中。这为信道状态信息的训练和估计引入了新的挑战。在RIS辅助系统中CSI获取的文献的快速增长一直伴随着基本假设，模型和符号的变化，这可能会掩盖各种技术的相似性和差异，以及它们的相对优点。本文全面阐述了RIS信道估计的原理和方法。每一类技术的基本思想被简化为一个统一的模型和符号下的最简单的形式，并在每个类的各种方法进行了讨论。在这一领域的几个开放的问题被确定和强调。
Reconfigurable intelligent surfaces (RIS) are passive controllable arrays of small reflectors that direct electromagnetic energy towards or away from the target nodes, thereby allowing better management of signals and interference in a wireless network. The RIS has the potential for significantly improving the performance of wireless networks. Unfortunately, RIS also multiplies the number of Channel State Information (CSI) coefficients between the transmitter and receiver, which magnifies the challenges in estimating and communicating the channel state information. Furthermore, the simplicity and cost-effectiveness of the passive RIS also implies that the incoming links are not locally estimated at the RIS, and fresh pilots are not inserted into outgoing RIS links. This introduces new challenges for training and estimation of channel state information. The rapid growth of the literature on CSI acquisition in RIS-aided systems has been accompanied by variations in the underlying assumptions, models, and notation, which can obscure the similarities and differences of various techniques, and their relative merits. This paper presents a comprehensive exposition of principles and approaches in RIS channel estimation. The basic ideas underlying each class of techniques are reduced to their simplest form under a unified model and notation, and various approaches within each class are discussed. Several open problems in this area are identified and highlighted.