发表机构
Commonwealth Cyber Initiative, Virginia Tech; Arizona State University（弗吉尼亚理工大学联邦网络倡议; 亚利桑那州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对RIS因码本存储限制难以自适应信道的问题，提出在微控制器上部署小型ML模型动态生成相位掩码，实现RIS辅助的自适应通信。
AI 中文摘要
可重构智能表面（RIS）因能被动反射入射信号，成为毫米波（mmWave）通信领域极具吸引力的技术。不过，当前RIS的实现依赖离线执行计算密集型算法来生成相位掩码，这些掩码会以码本形式存储在RIS的嵌入式微控制器中。码本大小受限于嵌入式微控制器的存储容量，这限制了RIS适应不断变化的信道条件和部署场景的能力。在本演示中，我们展示了一种基于机器学习（ML）的解决方案，用于在运行时动态生成新的相位掩码。我们的方法利用部署在微控制器上的ML模型来近似相位掩码生成算法的输出，可响应新输入，且模型规模小于码本。
英文摘要
Reconfigurable Intelligent Surfaces (RISs) are an attractive technology for Millimeter Wave (mmWave) communications due to their ability to passively reflect incident signals. However, current implementations of RIS rely on performing computationally-intensive algorithms offline to generate phase masks, which are stored as a codebook on the embedded microcontroller on the RIS. The codebook size is restricted by the embedded microcontroller's storage capacity, which limits the ability of the RIS to adapt to evolving channel conditions and deployment scenarios. In this demo, we showcase an Machine Learning (ML)-based solution for dynamically generating new phase masks during runtime. Our approach leverages a ML model deployed on the microcontroller for approximating the output of a phase mask generation algorithm, responding to new inputs while remaining smaller than a codebook.
