Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics

MaríaLuisa Marí-Altozano¹  JoséMaría Ruiz-Avilés¹  Salvador Luna-Ramírez¹  Carolina Gijón¹  Matías Toril¹ 

[1]Instituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, Spain

关键词: mobile network;    traffic forecasting;    network dimensioning;    time series;    supervised learning;   

DOI  :  10.3390/electronics10101151

来源: DOAJ

【 摘 要 】

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

【 授权许可】

Unknown