Modeling Cellular User Mobility Using a Leap Graph

  • Wei Dong
  • Nick Duffield
  • Zihui Ge
  • Seungjoon Lee
  • Jeffrey Pang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7799)


User mobility prediction can enable a mobile service provider to optimize the use of its network resources, e.g., through coordinated selection of base stations and intelligent content prefetching. In this paper, we study how to perform mobility prediction by leveraging the base station level location information readily available to a service provider. However, identifying real movements from handovers between base stations is non-trivial, because they can occur without actual user movement (e.g., due to signal fluctuation). To address this challenge, we introduce the leap graph, where an edge (or a leap) corresponds to actual user mobility. We present the properties of leap based mobility and demonstrate how it yields a mobility trace more suitable for mobility prediction. We evaluate mobility prediction on the leap graph using a Markov model based approach. We show that prediction using model can substantially improve the performance of content prefetching and base station selection during handover.


Mobile Device Macrocell Base Station Mobility Prediction Mobility Trace Cell Tower 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Wei Dong
    • 1
  • Nick Duffield
    • 2
  • Zihui Ge
    • 2
  • Seungjoon Lee
    • 2
  • Jeffrey Pang
    • 2
  1. 1.The University of Texas at AustinUSA
  2. 2.AT&T Labs – ResearchUSA

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