aalto1 untyped-item.component.html
Toward ML/AI-based prediction of mobile service usage in next-generation networks
Loading...
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
Series
IEEE Network, Volume 34, issue 4, pp. 106-111
Abstract
The adoption of machine learning techniques in next-generation networks has increasingly attracted the attention of the research community. This is to provide adaptive learning and decision-making approaches to meet the requirements of different verticals, and to guarantee the appropriate performance requirements in complex mobility scenarios. In this perspective, the characterization of mobile service usage represents a fundamental step. In this vein, this paper highlights the new features and capabilities offered by the "Network Slice Planner"(NSP) in its second version [12]. It also proposes a method combining both supervised and unsupervised learning techniques to analyze the behavior of a mass of mobile users in terms of service consumption. We exploit the data provided by the NSP v2 to conduct our analysis. Furthermore, we provide an evaluation of both the accuracy of the predictor and the performance of the underlying MEC infrastructure.
Description
| openaire: EC/H2020/871780/EU//MonB5G
Keywords
Other note
Citation
Taleb, T, Laghrissi, A & Bensalem, D E 2020, 'Toward ML/AI-based prediction of mobile service usage in next-generation networks', IEEE Network, vol. 34, no. 4, 9048615, pp. 106-111. https://doi.org/10.1109/MNET.001.1900462