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.

翻译：空时干扰是局域网的一个重要绩效指标,在特定时期内测量节点被迫等待其他传输后才能传送或接收的时间百分比。能够准确估计因特定状态变化(例如频道、带宽、电源)造成的干扰,可以更好地控制局域网资源,在实际执行之前评估特定配置的影响。在本文中,我们采用原则性办法对局域网进行干扰估计。我们首先使用真实数据来说明影响它的因素,并得出一套相关的合成工作量,以便从准确性、概括性和对局域网数据的稳健性等方面对各种深层学习结构进行控制性比较。我们毫不奇怪地发现,图动网络(GCNs)能够产生最佳的总体性能,利用园区网内固有的图形结构。我们注意到,与LSTMS不同的是,它们努力学习特定节点的行为,除非考虑到节点的索引。我们最后通过在培训时间应用经过训练的部署模式来核实了GCN模型的普及能力。