标题： 基于图神经网络的宽带用户调度与混合预编码学习 显示英文标题

标题： Learning Wideband User Scheduling and Hybrid Precoding with Graph Neural Networks

摘要： 用户调度和宽带多天线系统中的混合预编码从未被联合学习过，这是由于在资源块（RBs）上的大量用户组合以及RBs之间的共享模拟预编码所带来的挑战。 在本文中，我们努力使用图神经网络（GNNs）联合学习调度和预编码策略，由于其在跨问题规模泛化方面的潜力，GNNs已成为优化资源分配的强大工具。 通过将联合优化问题重新表述为调度和预编码策略的等效函数优化问题，我们提出了一种由两个级联模块组成的基于GNN的架构来学习这两种策略。 我们发现了一个相同参数相同决策（SPSD）属性，该属性适用于集合上的无线策略，揭示了当用户具有相似信道时，GNN无法很好地学习最优调度策略。 这促使我们开发了一系列GNN来增强调度器模块。 此外，通过分析SPSD属性，我们发现GNN中的线性聚合器会阻碍规模泛化。 基于这一观察，我们在预编码器模块的信息聚合中设计了一种新的注意力机制。 仿真结果表明，所提出的架构在推理时间短、训练复杂度低的情况下实现了满意的频谱效率，并且可以泛化到基站和用户的用户数、RB数和天线数。 显示英文摘要

摘要： User scheduling and hybrid precoding in wideband multi-antenna systems have never been learned jointly due to the challenges arising from the massive user combinations on resource blocks (RBs) and the shared analog precoder among RBs. In this paper, we strive to jointly learn the scheduling and precoding policies with graph neural networks (GNNs), which have emerged as a powerful tool for optimizing resource allocation thanks to their potential in generalizing across problem scales. By reformulating the joint optimization problem into an equivalent functional optimization problem for the scheduling and precoding policies, we propose a GNN-based architecture consisting of two cascaded modules to learn the two policies. We discover a same-parameter same-decision (SPSD) property for wireless policies defined on sets, revealing that a GNN cannot well learn the optimal scheduling policy when users have similar channels. This motivates us to develop a sequence of GNNs to enhance the scheduler module. Furthermore, by analyzing the SPSD property, we find when linear aggregators in GNNs impede size generalization. Based on the observation, we devise a novel attention mechanism for information aggregation in the precoder module. Simulation results demonstrate that the proposed architecture achieves satisfactory spectral efficiency with short inference time and low training complexity, and is generalizable to the numbers of users, RBs, and antennas at the base station and users.