Applying Machine Learning to LTE Traffic Prediction: Comparison of Bagging, Random Forest, and SVM

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
111 Downloads (Pure)

Abstract

Today, a significant share of smartphone applications use Artificial Intelligence (AI) elements that, in turn, are based on Machine Learning (ML) principles. Particularly, ML is also applied to the Edge paradigm aiming to predict and optimize the network load conventionally caused by human-based traffic, which is growing each year rapidly. The application of both standard and deep ML techniques is expected to improve the networks’ operation in the most complex heterogeneous environment. In this work, we propose a method to predict the LTE network edge traffic by utilizing various ML techniques. The analysis is based on the public cellular traffic dataset, and it presents a comparison of the quality metrics. The Support Vector Machines method allows much faster training than the Bagging and Random Forest that operate well with a mixture of numerical and categorical features.
Original languageEnglish
Title of host publication12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
PublisherIEEE
Pages119-123
Number of pages5
ISBN (Print)9781728192819
DOIs
Publication statusPublished - 14 Oct 2020
Publication typeA4 Article in conference proceedings
EventInternational Congress on Ultra Modern Telecommunications and Control Systems and Workshops - Brno, Czech Republic
Duration: 5 Oct 20207 Oct 2020

Publication series

NameInternational Conference on Ultra Modern Telecommunications & workshops
ISSN (Electronic)2157-023X

Conference

ConferenceInternational Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Country/TerritoryCzech Republic
CityBrno
Period5/10/207/10/20

Keywords

  • Machine Learning
  • traffic analysis
  • LTE
  • NGN
  • optimisation

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering

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