Toward ML/AI-based prediction of mobile service usage in next-generation networks
Taleb, Tarik; Laghrissi, Abdelquoddouss; Bensalem, Djamel Eddine (2020-03-27)
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
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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.
- Avoin saatavuus