Abstract
Millimeter-wave (mmWave) communication is a promising technology that has become a key component of next-generation wireless networks due to its large available band-width. However, the susceptibility of mmWave link to dynamic blockages makes it challenging to maintain consistently high rate performance. Hence, it is imperative to have the knowledge of link quality in advance at the location of interest to proactively optimize the use of network resources. In this work, we propose a Spatial-Temporal Attention-based Prediction (STAP) framework to predict the link quality at arbitrary locations in the presence of dynamic blockages. Specifically, our STAP model is built to capture the spatial correlation and temporal dependency of mmWave wireless characteristics in an integrated module, followed by an attention mechanism to complement the link quality prediction task. On top of that, we also design a regional training approach with a weighted loss function to address the data imbalance problem of map-based prediction. Extensive evaluation results show that our framework effectively captures comprehensive spatial-temporal knowledge and achieves significantly higher accuracy than other baseline prediction methods.