机器学习和可重构智能表面的相互作用展望：机会和限制概述
An Outlook on the Interplay of Machine Learning and Reconfigurable Intelligent Surfaces: An Overview of Opportunities and Limitations
摘要：
可编程元表面（也称为可重构智能表面（RIS））的最新进展被设想为提供从不可控到完全可调和可定制的无线传播环境的范式转变，从而实现大量的新应用和技术趋势。因此，鉴于这一前沿的技术概念，我们首先审查的架构和电磁波操纵功能的RIS。然后，我们详细介绍了一些最近取得的进展，实现这些可编程功能的无线通信应用。此外，我们详细介绍了机器学习（ML）如何解决由实时部署RIS引入的各种限制，特别是在延迟，存储，能源效率和计算方面。回顾了ML与RISs集成的最新研究，强调了它们的潜力和挑战。最后，本文的结论是提供了一个前瞻性的未开发的可能性ML机制的背景下，RISs。
英文摘要：
Recent advances in programmable metasurfaces, also dubbed as reconfigurable intelligent surfaces (RISs), are envisioned to offer a paradigm shift from uncontrollable to fully tunable and customizable wireless propagation environments, enabling a plethora of new applications and technological trends. Therefore, in view of this cutting edge technological concept, we first review the architecture and electromagnetic waves manipulation functionalities of RISs. We then detail some of the recent advancements that have been made towards realizing these programmable functionalities in wireless communication applications. Furthermore, we elaborate on how machine learning (ML) can address various constraints introduced by the real-time deployment of RISs, particularly in terms of latency, storage, energy efficiency, and computation. A review of the state-of-the-art research on the integration of ML with RISs is presented, highlighting their potentials as well as challenges. Finally, the paper concludes by offering a look ahead towards unexplored possibilities of ML mechanisms in the context of RISs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
