AI powered solution for radio link failure prediction based on link features and weather forecast

Abstract

Radio link sustainability gets affected by weather adversities such as snow, fog, cloud, rain, thunderstorm, etc. A proactive solution in radio link failure scenarios is necessary to overcome economic loss and maintain the Quality of Service (QoS). To address the issue, our work contributes towards building a machine-learning-based solution to predict the radio link failure when generic regional weather forecast data, key performance indices of radio link and spatial nature of the data are available. After rigorous data preprocessing, ensembling models like logistic regression, random forest, light BGM, XGBoost and gradient boosting classifiers were trained to predict the Radio Link Failure (RLF) for two cases i.e., day-1-predict and day-5-predict. Since it is a classification use case, the metrics used for our work are precision, recall, and F1 score. The performance of the gradient boosting classifier was better as compared to the other models with an F1 score of 0.95 for both day-1-predict and day-5-predict.