  Radio link failure (RLF) prediction system in Radio Access Networks (RANs) is
critical for ensuring seamless communication and meeting the stringent
requirements of high data rates, low latency, and improved reliability in 5G
networks. However, weather conditions such as precipitation, humidity,
temperature, and wind impact these communication links. Usually, historical
radio link Key Performance Indicators (KPIs) and their surrounding weather
station observations are utilized for building learning-based RLF prediction
models. However, such models must be capable of learning the spatial weather
context in a dynamic RAN and effectively encoding time series KPIs with the
weather observation data. Existing works fail to incorporate both of these
essential design aspects of the prediction models. This paper fills the gap by
proposing GenTrap, a novel RLF prediction framework that introduces a graph
neural network (GNN)-based learnable weather effect aggregation module and
employs state-of-the-art time series transformer as the temporal feature
extractor for radio link failure prediction. The proposed aggregation method of
GenTrap can be integrated into any existing prediction model to achieve better
performance and generalizability. We evaluate GenTrap on two real-world
datasets (rural and urban) with 2.6 million KPI data points and show that
GenTrap offers a significantly higher F1-score (0.93 for rural and 0.79 for
urban) compared to its counterparts while possessing generalization capability.
