Towards 5G and beyond radio link diagnosis: Radio link failure prediction by using historical weather, link parameters. (April 2022)

• Record Type: Journal Article
• Authors: Aktaş, Semih; Alemdar, Hande; Ergüt, Salih
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Radio link failure -- Next-generation network -- Machine learning -- LSTM; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107742
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Weather-related phenomena such as clouds, rain, snow affect the performance of radio links. To reduce the adverse effects of radio link failures' on the user experience, mobile operators require intelligent monitoring systems to predict link failures and take actions before they happen. In this study, we show how machine learning can be used for prediction using a real-world telecom operator dataset. We propose a novel architecture to process time-series data and non-times-series data together in the same neural model to have better performance in predictions. We compare our model with the traditional approaches such as logistic regression (LR), support vector machines (SVM), and Long Short-term Memory (LSTM). Through experimental evaluations, we show that the F1-score of our proposed model is 0.638, whereas for the pure LSTM model it is 0.601. SVM and LR methods perform significantly worse with F1 scores of 0.455 and 0.105, respectively. Graphical abstract: