论文标题
QT-Routenet：通过对排队理论进行微调预测，改进了GNN对更大的5G网络的概括
QT-Routenet: Improved GNN generalization to larger 5G networks by fine-tuning predictions from queueing theory
论文作者
论文摘要
为了促进5G的机器学习，国际电信联盟（ITU）在2021年提议的第二版是5G挑战中ITU AI/ML的第二版，来自82个国家/地区的1600多名参与者。这项工作详细介绍了第二名的整体解决方案，这也是图形神经网络挑战2021的获胜解决方案。当将模型应用于5G网络时，我们解决了泛化问题，该模型可能具有比训练中观察到的更长的路径和更大的链接容量。为了实现这一目标，我们建议首先提取与排队理论（QT）相关的鲁棒特征，然后使用Routenet Graph神经网络（GNN）模型的修改来微调分析基线预测。所提出的解决方案比简单地使用Routenet更好地概括了，并且设法将分析基线的10.42平均绝对百分比误差降低到1.45（合奏为1.27）。这表明，对已知鲁棒的近似模型进行小更改可能是提高准确性的有效方法，而不会损害概括。
In order to promote the use of machine learning in 5G, the International Telecommunication Union (ITU) proposed in 2021 the second edition of the ITU AI/ML in 5G challenge, with over 1600 participants from 82 countries. This work details the second place solution overall, which is also the winning solution of the Graph Neural Networking Challenge 2021. We tackle the problem of generalization when applying a model to a 5G network that may have longer paths and larger link capacities than the ones observed in training. To achieve this, we propose to first extract robust features related to Queueing Theory (QT), and then fine-tune the analytical baseline prediction using a modification of the Routenet Graph Neural Network (GNN) model. The proposed solution generalizes much better than simply using Routenet, and manages to reduce the analytical baseline's 10.42 mean absolute percent error to 1.45 (1.27 with an ensemble). This suggests that making small changes to an approximate model that is known to be robust can be an effective way to improve accuracy without compromising generalization.
