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
|Author:||Taleb, Tarik1,2,3; Laghrissi, Abdelquoddouss2; Bensalem, Djamel Eddine1|
1Aalto University, Espoo, Finland
2Oulu University, Oulu, Finland
3Sejong University, Seoul, Korea
|Online Access:||PDF Full Text (PDF, 0.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020100983552
Institute of Electrical and Electronics Engineers,
|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.
|Pages:||106 - 111|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
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 (Academy of Finland Funding decision)
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