Call details record analysis: A spatiotemporal exploration toward mobile traffic classification and optimization

Kashif Sultan, Hazrat Ali, Adeel Ahmad, Zhongshan Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia. Thus, on the basis of spatiotemporal insights, we propose a framework for mobile traffic classification. Experimental results show that the proposed model based on machine learning technique is able to accurately model and classify the network traffic patterns. Furthermore, we demonstrate the application of such insights for resource optimisation.

Original languageEnglish
Article number192
JournalInformation (Switzerland)
Volume10
Issue number6
DOIs
Publication statusPublished - 1 Jun 2019
Externally publishedYes

Keywords

  • Call details record
  • Data analytics
  • Machine learning
  • Mobile networks

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