基于图神经网络的分布式调度
Distributed Scheduling using Graph Neural Networks
摘要：
无线网络设计中的一个基本问题是以分布式方式有效地调度传输。主要的挑战源于这样一个事实，即最优链路调度涉及解决最大加权独立集（MWIS）问题，这是NP-难。对于实际的链路调度方案，分布式贪婪方法通常用于近似的MWIS问题的解决方案。然而，这些贪婪的方案大多忽略了重要的无线网络的拓扑信息。为了克服这一限制，我们提出了一个分布式MWIS求解器的基础上图卷积网络（GCN）。简而言之，可训练的GCN模块在调用贪婪求解器之前学习与网络权重相结合的拓扑感知节点嵌入。在具有数十个链路的中小型无线网络中，即使是基于浅GCN的MWIS调度器也可以利用图的拓扑信息来将分布式贪婪求解器的次优性差距减少一半，同时具有良好的跨图泛化能力和最小的复杂性增加。
英文摘要：
A fundamental problem in the design of wireless networks is to efficiently schedule transmission in a distributed manner. The main challenge stems from the fact that optimal link scheduling involves solving a maximum weighted independent set (MWIS) problem, which is NP-hard. For practical link scheduling schemes, distributed greedy approaches are commonly used to approximate the solution of the MWIS problem. However, these greedy schemes mostly ignore important topological information of the wireless networks. To overcome this limitation, we propose a distributed MWIS solver based on graph convolutional networks (GCNs). In a nutshell, a trainable GCN module learns topology-aware node embeddings that are combined with the network weights before calling a greedy solver. In small- to middle-sized wireless networks with tens of links, even a shallow GCN-based MWIS scheduler can leverage the topological information of the graph to reduce in half the suboptimality gap of the distributed greedy solver with good generalizability across graphs and minimal increase in complexity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
