| Autor: |
Antonio Massaro, Dimitre Kostadinov, Alonso Silva, Alexander Obeid Guzman, Armen Aghasaryan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Entropy, Vol 25, Iss 6, p 917 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1099-4300 |
| DOI: |
10.3390/e25060917 |
| Popis: |
Maintaining and managing ever more complex telecommunication networks is an increasingly difficult task, which often challenges the capabilities of human experts. There is a consensus both in academia and in the industry on the need to enhance human capabilities with sophisticated algorithmic tools for decision-making, with the aim of transitioning towards more autonomous, self-optimizing networks. We aimed to contribute to this larger project. We tackled the problem of detecting and predicting the occurrence of faults in hardware components in a radio access network, leveraging the alarm logs produced by the network elements. We defined an end-to-end method for data collection, preparation, labelling, and fault prediction. We proposed a layered approach to fault prediction: we first detected the base station that is going to be faulty and at a second stage, and using a different algorithm, we detected the component of the base station that is going to be faulty. We designed a range of algorithmic solutions and tested them on real data collected from a major telecommunication operator. We concluded that we are able to predict the failure of a network component with satisfying precision and recall. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
|
Nepřihlášeným uživatelům se plný text nezobrazuje |
K zobrazení výsledku je třeba se přihlásit.
|
