An Approach to Data Analysis in 5G Networks

Impacto

Downloads

Downloads per month over past year

Barona López, Lorena Isabel and Maestre Vidal, Jorge and García Villalba, Luis Javier (2017) An Approach to Data Analysis in 5G Networks. Entropy, 19 (2). p. 74. ISSN 1099-4300

[thumbnail of entropy-19-00074.pdf]
Preview
PDF
Creative Commons Attribution.

1MB

Official URL: https://doi.org/10.3390/e19020074




Abstract

5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.


Item Type:Article
Uncontrolled Keywords:5G; data analysis; network function virtualization; situational awareness; software defined networking
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science > Internet
Sciences > Computer science > Computer networks
Sciences > Computer science > Software
ID Code:67589
Deposited On:01 Sep 2021 07:48
Last Modified:06 Sep 2021 09:10

Origin of downloads

Repository Staff Only: item control page