Airtime interference is a key performance indicator for WLANs, measuring, for
a given time period, the percentage of time during which a node is forced to
wait for other transmissions before to transmitting or receiving. Being able to
accurately estimate interference resulting from a given state change (e.g.,
channel, bandwidth, power) would allow a better control of WLAN resources,
assessing the impact of a given configuration before actually implementing it.
In this paper, we adopt a principled approach to interference estimation in
WLANs. We first use real data to characterize the factors that impact it, and
derive a set of relevant synthetic workloads for a controlled comparison of
various deep learning architectures in terms of accuracy, generalization and
robustness to outlier data. We find, unsurprisingly, that Graph Convolutional
Networks (GCNs) yield the best performance overall, leveraging the graph
structure inherent to campus WLANs. We notice that, unlike e.g. LSTMs, they
struggle to learn the behavior of specific nodes, unless given the node indexes
in addition. We finally verify GCN model generalization capabilities, by
applying trained models on operational deployments unseen at training time.

本文采用基于实际数据的方法，研究 WLAN 中的干扰估计问题。并通过分析各种深度学习体系结构在准确性、泛化性和对异常数据的鲁棒性方面的控制比较，结论是图卷积网络（GCN）表现最佳，但如果不得不给出节点索引，则无法学习特定节点的行为。同时，还验证了 GCN 模型的泛化能力。