Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series
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 adv...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/10/1151 |
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| author | Carolina Gijón Matías Toril Salvador Luna-Ramírez María Luisa Marí-Altozano José María Ruiz-Avilés |
| author_facet | Carolina Gijón Matías Toril Salvador Luna-Ramírez María Luisa Marí-Altozano José María Ruiz-Avilés |
| author_sort | Carolina Gijón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T11:30:00Z |
| format | Article |
| id | doaj.art-b5f2594395ce4382a1af92a00ecea5eb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T11:30:00Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-b5f2594395ce4382a1af92a00ecea5eb2023-11-21T19:20:24ZengMDPI AGElectronics2079-92922021-05-011010115110.3390/electronics10101151Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time SeriesCarolina Gijón0Matías Toril1Salvador Luna-Ramírez2María Luisa Marí-Altozano3José María Ruiz-Avilés4Instituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, SpainInstituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, SpainInstituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, SpainInstituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, SpainInstituto de Telecomunicaciones (TELMA), Universidad de Málaga, CEI Andalucía TECH, 29071 Málaga, SpainNetwork 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.https://www.mdpi.com/2079-9292/10/10/1151mobile networktraffic forecastingnetwork dimensioningtime seriessupervised learning |
| spellingShingle | Carolina Gijón Matías Toril Salvador Luna-Ramírez María Luisa Marí-Altozano José María Ruiz-Avilés Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series Electronics mobile network traffic forecasting network dimensioning time series supervised learning |
| title | Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series |
| title_full | Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series |
| title_fullStr | Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series |
| title_full_unstemmed | Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series |
| title_short | Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series |
| title_sort | long term data traffic forecasting for network dimensioning in lte with short time series |
| topic | mobile network traffic forecasting network dimensioning time series supervised learning |
| url | https://www.mdpi.com/2079-9292/10/10/1151 |
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