The requirement of wireless data demands is increasingly high as the
sixth-generation (6G) technology evolves. Reconfigurable intelligent surface
(RIS) is promisingly deemed to be one of 6G techniques for extending service
coverage, reducing power consumption, and enhancing spectral efficiency. In
this article, we have provided some fundamentals of RIS deployment in theory
and hardware perspectives as well as utilization of artificial intelligence
(AI) and machine learning. We conducted an intelligent deployment of RIS
(i-Dris) prototype, including dual-band auto-guided vehicle (AGV) assisted RISs
associated with an mmWave base station (BS) and a receiver. The RISs are
deployed on the AGV with configured incident/reflection angles. While, both the
mmWave BS and receiver are associated with an edge server monitoring downlink
packets for obtaining system throughput. We have designed a federated
multi-agent reinforcement learning scheme associated with several AGV-RIS
agents and sub-agents per AGV-RIS consisting of the deployment of position,
height, orientation and elevation angles. The experimental results presented
the stationary measurement in different aspects and scenarios. The i-Dris can
reach up to 980 Mbps transmission throughput under a bandwidth of 100 MHz with
comparably low complexity as well as rapid deployment, which outperforms the
other existing works. At last, we highlight some opportunities and future
issues in leveraging RIS-empowered wireless communication networks.

本文提供了 RIS 在 6G 技术中的部署及其与人工智能和机器学习的利用的基础知识，并通过智能部署的 RIS 原型实验验证了其在增强系统吞吐量方面的性能优势和快速部署能力。

基于人工智能的无人车辅助可重构智能表面：部署、原型制作、实验与机遇

AI-Enabled Unmanned Vehicle-Assisted Reconfigurable Intelligent  Surfaces: Deployment, Prototyping, Experiments, and Opportunities

A realistic performance assessment of any wireless technology requires the
use of a channel model that reflects its main characteristics. The independent
and identically distributed Rayleigh fading channel model has been (and still
is) the basis of most theoretical research on multiple antenna technologies in
scattering environments. This letter shows that such a model is not physically
appearing when using a reconfigurable intelligent surface (RIS) with
rectangular geometry and provides an alternative physically feasible Rayleigh
fading model that can be used as a baseline when evaluating RIS-aided
communications. The model is used to revisit the basic RIS properties, e.g.,
the rank of spatial correlation matrices and channel hardening.

本文评估了使用可重构智能表面（RIS）时，独立同分布的瑞利衰落信道模型不再是基于散射环境中的多天线技术的理论研究的基础，并提出了一种可用于评估 RIS 辅助通信的可行的瑞利衰落模型。该模型用于重新审视 RIS 的基本属性，例如，空间相关矩阵的秩和信道硬化。