Predicting the throughput of WLAN deployments is a classic problem that occurs in the design of robust and high performance WLAN systems. However, due to the increasingly complex communication protocols and the increase in interference between devices in denser and denser WLAN deployments, traditional methods either have substantial runtime or enormous prediction error and hence cannot be applied in downstream tasks. Recently, Graph Neural Networks have been proven to be powerful graph analytic models and have been broadly applied to various networking problems such as link scheduling and power allocation. In this work, we propose HTNet, a specialized Heterogeneous Temporal Graph Neural Network that extracts features from dynamic WLAN deployments. Analyzing the unique graph structure of WLAN deployment graphs, we show that HTNet achieves the maximum expressive power on each snapshot. Based on a powerful message passing scheme, HTNet requires fewer number of layers compared with other GNN-based methods which entails less supporting data and runtime. To evaluate the performance of HTNet, we prepare six different setups with more than five thousands dense dynamic WLAN deployments that cover a wide range of real-world scenarios. HTNet achieves the lowest prediction error on all six setups with an average improvement of 25.3\% over the state-of-the-art methods.

翻译：预测WLAN部署的吞吐量是一个经典问题，出现在鲁棒且高性能WLAN系统的设计中。然而，由于日益复杂的通信协议以及越来越密集的WLAN部署中设备间干扰的增加，传统方法要么运行时间过长，要么预测误差巨大，因此无法应用于下游任务。近年来，图神经网络已被证明是强大的图分析模型，并广泛应用于链路调度、功率分配等各种网络问题。在本工作中，我们提出HTNet，一种专门从动态WLAN部署中提取特征的异质时序图神经网络。通过分析WLAN部署图的独特图结构，我们证明HTNet在每个快照上都能达到最大表达能力。基于强大的消息传递机制，与其他基于GNN的方法相比，HTNet所需的层数更少，因此需要更少的支持数据和运行时间。为评估HTNet的性能，我们准备了六种不同的设置，涵盖超过五千个密集动态WLAN部署，覆盖广泛的现实场景。HTNet在所有六种设置上均实现了最低的预测误差，平均比最先进方法提升25.3%。