  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.
