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

研究成果: Conference contribution同行評審

25 引文 斯高帕斯(Scopus)

摘要

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.

原文American English
主出版物標題CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1-4
頁數4
ISBN(電子)9781538647905
DOIs
出版狀態Published - 16 3月 2018
事件15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018 - Las Vegas, United States
持續時間: 12 1月 201815 1月 2018

出版系列

名字CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference
2018-January

Conference

Conference15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
國家/地區United States
城市Las Vegas
期間12/01/1815/01/18

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