A practical model for traffic forecasting based on big data, machine-learning, and network KPIs

Luong Vy Le, Do Sinh, Li Ping Tung, Bao-Shuh Lin 

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

12 Scopus citations

Abstract

Traffic forecasting plays an important role in improving network quality and energy saving of mobile networks. In 5G, traffic forecasting directly influences the self-organizing network (SON) in managing and controlling the network effectively. Especially, long-Term traffic forecasting can provide a detailed pattern of future traffic, besides permitting more time for planning and optimizing. Most of the traffic forecasting models used the history of traffic, while the utilization of another network KPIs (key performance indicators) for traffic forecasting is limited. Therefore, the authors propose here a practical platform and process for traffic forecasting, based on big data, machine-learning (ML), and network KPIs that are flexible to forecast accurately different statistical traffic characteristics of different types of cells (GSM, 3G, 4G) for both long-and short-Term forecasting. The performance of the proposed model was evaluated by applying it to a real dataset that collected KPIs of more than 6000 cells of a real network during the years, 2016 and 2017.

Original languageEnglish
Title of host publicationCCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538647905
DOIs
StatePublished - 16 Mar 2018
Event15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018 - Las Vegas, United States
Duration: 12 Jan 201815 Jan 2018

Publication series

NameCCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
Volume2018-January

Conference

Conference15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
CountryUnited States
CityLas Vegas
Period12/01/1815/01/18

Keywords

  • Big data
  • Machine Learning
  • SON
  • Traffic forecasting
  • key performance indicators (KPIs)

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