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 have implemented 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.

中文翻译：

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支持人工智能的无人驾驶车辆辅助可重构智能表面：部署、原型设计、实验和机遇


随着第六代（6G）技术的发展，无线数据需求越来越高。可重构智能表面（RIS）有望成为扩展服务覆盖范围、降低功耗和提高频谱效率的6G技术之一。在本文中，我们从理论和硬件角度以及人工智能 (AI) 和机器学习的利用方面提供了 RIS 部署的一些基础知识。我们已经实现了 RIS (i-Dris) 原型的智能部署，包括与毫米波基站 (BS) 和接收器相关的双频自动导引车 (AGV) 辅助 RIS。 RIS 部署在 AGV 上，并配置了入射/反射角度。同时，毫米波基站和接收器都与边缘服务器相关联，监控下行链路数据包以获得系统吞吐量。我们设计了一种联合多智能体强化学习方案，与多个 AGV-RIS 智能体和每个 AGV-RIS 的子智能体相关联，包括位置、高度、方向和仰角的部署。实验结果呈现了不同方面和场景下的静态测量。 i-Dris可以在100 MHz带宽下达到高达980 Mbps的传输吞吐量，并且复杂度相对较低，并且部署快速，优于其他现有作品。最后，我们强调了利用 RIS 授权的无线通信网络的一些机遇和未来问题。