University of Oulu

T. Taleb, A. Laghrissi and D. E. Bensalem, "Toward ML/AI-Based Prediction of Mobile Service Usage in Next-Generation Networks," in IEEE Network, vol. 34, no. 4, pp. 106-111, July/August 2020, doi: 10.1109/MNET.001.1900462

Toward ML/AI-based prediction of mobile service usage in next-generation networks

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Author: Taleb, Tarik1,2,3; Laghrissi, Abdelquoddouss2; Bensalem, Djamel Eddine1
Organizations: 1Aalto University, Espoo, Finland
2Oulu University, Oulu, Finland
3Sejong University, Seoul, Korea
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-10-09


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. 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.

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Series: IEEE network
ISSN: 0890-8044
ISSN-E: 1558-156X
ISSN-L: 0890-8044
Volume: 34
Issue: 4
Pages: 106 - 111
DOI: 10.1109/MNET.001.1900462
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This paper was partially supported by the European Unions Horizon 2020 Research and Innovation Program through the MonB5G Project under Grant No. 871780. This work was also supported in part by the Academy of Finland 6Genesis project under Grant No. 318927 and by the Academy of Finland CSN project under Grant No. 311654.
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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