标题： 一种可扩展的机器学习方法通过隐式信道估计实现RIS优化 显示英文标题

标题： A Scalable Machine Learning Approach Enabled RIS Optimization with Implicit Channel Estimation

摘要： 可重构智能表面（RIS）被视为下一代移动无线系统的关键使能技术。 由于其被动特性和低成本，RIS吸引了学术界和工业界的广泛关注，但RIS元件的可扩展性和对信道状态信息（CSI）的要求是RIS实现的两大主要困难。 在本工作中，我们引入了一种无监督机器学习（ML）增强的优化方法来配置RIS。 专用的神经网络（NN）架构RISnet与隐式信道估计方法相结合。 RISnet直接从接收到的导频信号学习映射到RIS配置，而无需显式信道估计。 仿真结果表明，所提出的算法显著优于基线方法。 显示英文摘要

摘要： The reconfigurable intelligent surface (RIS) is considered as a key enabler of the next-generation mobile radio systems. While attracting extensive interest from academia and industry due to its passive nature and low cost, scalability of RIS elements and requirement for channel state information (CSI) are two major difficulties for the RIS to become a reality. In this work, we introduce an unsupervised machine learning (ML) enabled optimization approach to configure the RIS. The dedicated neural network (NN) architecture RISnet is combined with an implicit channel estimation method. The RISnet learns to map from received pilot signals to RIS configuration directly without explicit channel estimation. Simulation results show that the proposed algorithm outperforms baselines significantly.