Forecasting the capacity of mobile networks

João Bastos

Telecommunication Systems: Modelling, Analysis, Design and Management, 2019, vol. 72, issue 2, No 6, 242 pages

Abstract: Abstract The optimization of mobile network capacity usage is an essential operation to promote positive returns on network investments, prevent capacity bottlenecks, and deliver good end user experience. This study examines the performance of several statistical models to predict voice and data traffic in a mobile network. While no method dominates the others across all time series and prediction horizons, exponential smoothing and ARIMA models are good alternatives to forecast both voice and data traffic. This analysis shows that network managers have at their disposal a set of statistical tools to plan future capacity upgrades with the most effective solution, while optimizing their investment and maintaining good network quality.

Keywords: Mobile networks; Forecasting practice; ARIMA models; Exponential smoothing; Time series (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11235-019-00556-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: Forecasting the capacity of mobile networks (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:72:y:2019:i:2:d:10.1007_s11235-019-00556-w

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-019-00556-w

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().