A Generalized Transformer-based Radio Link Failure Prediction Framework in 5G RANs

TL;DR

This paper introduces GenTrap, a transformer-based framework with a graph neural network component for improved radio link failure prediction in 5G networks, effectively incorporating weather effects and spatial-temporal data.

Contribution

The paper presents a novel RLF prediction framework combining GNN-based weather effect aggregation with a transformer for temporal features, enhancing accuracy and generalization.

Findings

GenTrap achieves higher F1-scores (0.93 rural, 0.79 urban) than existing models.

The aggregation module improves performance and can be integrated into other models.

GenTrap demonstrates strong generalization on large real-world datasets.

Abstract

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.