标题： 具有多个可重构智能表面的无线网络中的相位配置学习 显示英文标题

标题： Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces

摘要： 可重构智能表面（RISs）最近因其低成本、硬件高效和高度可扩展的特性而受到广泛关注，这些特性使其能够对电磁波传播进行动态控制。 它们在原本被动的无线通信环境中，通过在各种障碍物上的密集部署，被视为一种革命性的方法，将其转变为具有可配置特性的网络实体，为多种通信目标提供增强的环境智能。 在RIS赋能的无线通信中，一个主要挑战是多个RISs的低开销动态配置，根据当前的硬件设计，这些RISs的计算和存储能力非常有限。 在本文中，我们考虑由多个RISs辅助的两个节点之间的典型通信对，并为RISs的相位配置设计了低复杂度的监督学习方法。 通过假设每个RIS的单元元件组中存在公共可调相位，我们提出了多层感知器神经网络（NN）架构，这些架构可以使用定位值或瞬时信道系数进行训练。 我们研究了RISs的集中式和个体训练，以及它们的联邦，评估了它们的计算需求。 我们的仿真结果，包括与最优相位配置方案的比较，展示了在链路预算性能提升方面，在RISs上采用个体NN的优势。 显示英文摘要

摘要： Reconfigurable Intelligent Surfaces (RISs) are recently gaining remarkable attention as a low-cost, hardware-efficient, and highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. Their envisioned dense deployment over various obstacles of the, otherwise passive, wireless communication environment has been considered as a revolutionary means to transform them into network entities with reconfigurable properties, providing increased environmental intelligence for diverse communication objectives. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs, which according to the current hardware designs have very limited computing and storage capabilities. In this paper, we consider a typical communication pair between two nodes that is assisted by a plurality of RISs, and devise low-complexity supervised learning approaches for the RISs' phase configurations. By assuming common tunable phases in groups of each RIS's unit elements, we present multi-layer perceptron Neural Network (NN) architectures that can be trained either with positioning values or the instantaneous channel coefficients. We investigate centralized and individual training of the RISs, as well as their federation, and assess their computational requirements. Our simulation results, including comparisons with the optimal phase configuration scheme, showcase the benefits of adopting individual NNs at RISs for the link budget performance boosting.