RT Journal Article
T1 Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series
A1 Gijón, Carolina
A1 Toril-Genovés, Matías
A1 Luna-Ramírez, Salvador
A1 Marí-Altozano, María Luisa
A1 Ruiz-Avilés, José María
K1 Sistemas de comunicación móviles
AB 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
PB MDPI
YR 2021
FD 2021-05-12
LK https://hdl.handle.net/10630/37552
UL https://hdl.handle.net/10630/37552
LA eng
NO Gijón, C.; Toril, M.; Luna-Ramírez, S.; Marí-Altozano, M.L.; Ruiz-Avilés, J.M. Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series. Electronics 2021, 10, 1151. https://doi.org/10.3390/electronics10101151
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026