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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
A Practical Model for Traffic Forecasting based on Big Data, Machine-learning, and Network KPIs
ترجمه فارسی عنوان مقاله:
یک مدل عملی برای پیش بینی ترافیک بر اساس داده های بزرگ، ماشین های یادگیری و KPI های شبکه
منبع:
IEEE - 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)
نویسنده:
Luong-Vy Le1, Do Sinh2, Li-Ping Tung3, Bao-Shuh Paul Lin2
چکیده انگلیسی:
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, machinelearning (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
Keywords: key performance indicators (KPIs); Traffic forecasting; Machine Learning; SON; Big data
قیمت: رایگان
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