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

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

22 Sitaatiot (Scopus)
121 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
Otsikko12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)
KustantajaIEEE
Sivut119-123
Sivumäärä5
ISBN (painettu)9781728192819
DOI - pysyväislinkit
TilaJulkaistu - 14 lokak. 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaInternational Congress on Ultra Modern Telecommunications and Control Systems and Workshops - Brno, Tshekki
Kesto: 5 lokak. 20207 lokak. 2020

Julkaisusarja

NimiInternational Conference on Ultra Modern Telecommunications & workshops
ISSN (elektroninen)2157-023X

Conference

ConferenceInternational Congress on Ultra Modern Telecommunications and Control Systems and Workshops
Maa/AlueTshekki
KaupunkiBrno
Ajanjakso5/10/207/10/20

Julkaisufoorumi-taso

  • Jufo-taso 1

!!ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering

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