标题： 基于学习的可重构智能表面辅助多用户下行链路用户调度 显示英文标题

标题： Learning Based User Scheduling in Reconfigurable Intelligent Surface Assisted Multiuser Downlink

摘要： 可重构智能表面（RIS）能够智能地操控入射电磁波的相位，以改善基站（BS）和用户之间的无线传播环境。 本文研究了在具有有限导频开销的RIS辅助下行网络中的联合用户调度、RIS配置和BS波束成形问题。 我们表明，具有排列不变性和等变性的图神经网络（GNN）可以用于适当调度用户并设计RIS配置，以在考虑用户之间公平性的同时实现高总体吞吐量。 与传统方法首先估计信道然后优化用户调度、RIS配置和波束成形相比，本文表明可以通过使用GNN从非常短的一组导频中直接获得优化的用户调度，然后使用第二个GNN优化RIS配置，最后根据整体有效信道设计BS波束成形器。 数值结果表明，所提出的方法比基于信道估计的传统方法更高效地利用接收到的导频，并且可以推广到具有任意数量用户的系统。 显示英文摘要

摘要： Reconfigurable intelligent surface (RIS) is capable of intelligently manipulating the phases of the incident electromagnetic wave to improve the wireless propagation environment between the base-station (BS) and the users. This paper addresses the joint user scheduling, RIS configuration, and BS beamforming problem in an RIS-assisted downlink network with limited pilot overhead. We show that graph neural networks (GNN) with permutation invariant and equivariant properties can be used to appropriately schedule users and to design RIS configurations to achieve high overall throughput while accounting for fairness among the users. As compared to the conventional methodology of first estimating the channels then optimizing the user schedule, RIS configuration and the beamformers, this paper shows that an optimized user schedule can be obtained directly from a very short set of pilots using a GNN, then the RIS configuration can be optimized using a second GNN, and finally the BS beamformers can be designed based on the overall effective channel. Numerical results show that the proposed approach can utilize the received pilots more efficiently than the conventional channel estimation based approach, and can generalize to systems with an arbitrary number of users.