A Generalized GNN-Transformer-Based Radio Link Failure Prediction Framework in 5G RAN

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 work utilizes a heuristic-based and non-generalizable weather station aggregation method that uses Long Short-Term Memory (LSTM) for non-weighted sequence modeling. This paper fills the gap by proposing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GenTrap</i>, a novel RLF prediction framework that introduces a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Graph Neural Network (GNN)</i>-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 GNN module encodes surrounding weather station data of each radio site while the transformer module encodes historical radio and weather observation features. 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 of 0.93 for rural and 0.79 for urban, an increase of 29% and 21% respectively, compared to the state-of-the-art LSTM-based solutions while offering a 20% increased generalization capability.