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

With the application of the fifth-generation wireless communication
technologies, more smart terminals are being used and generating huge amounts
of data, which has prompted extensive research on how to handle and utilize
these wireless data. Researchers currently focus on the research on the
upper-layer application data or studying the intelligent transmission methods
concerning a specific problem based on a large amount of data generated by the
Monte Carlo simulations. This article aims to understand the endogenous
relationship of wireless data by constructing a knowledge graph according to
the wireless communication protocols, and domain expert knowledge and further
investigating the wireless endogenous intelligence. We firstly construct a
knowledge graph of the endogenous factors of wireless core network data
collected via a 5G/B5G testing network. Then, a novel model based on graph
convolutional neural networks is designed to learn the representation of the
graph, which is used to classify graph nodes and simulate the relation
prediction. The proposed model realizes the automatic nodes classification and
network anomaly cause tracing. It is also applied to the public datasets in an
unsupervised manner. Finally, the results show that the classification accuracy
of the proposed model is better than the existing unsupervised graph neural
network models, such as VGAE and ARVGE.

本文利用 5G/B5G 测试网络采集数据，构建了一个基于知识图谱和图卷积神经网络的自动节点分类和网络异常原因追踪模型，该模型应用于公共数据集上的无监督学习中取得了比现有模型更好的分类精度。