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Understanding and Predicting Data Hotspots in Cellular Networks

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Abstract

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today’s cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We show that using standard machine learning methods, future hotspots can be accurately predicted from past observations. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to empirically characterize traffic hotspots in today’s cellular networks.

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Notes

  1. The use of haversine distance helps to cluster cells separated by a small physical distance.

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Authors and Affiliations

  1. Department of Computer Science, University of California, Santa Barbara, CA, 93106-5110, USA

    Ana Nika, Asad Ismail, Ben Y. Zhao & Haitao Zheng

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito & Gian Paolo Rossi

Authors
  1. Ana Nika
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  2. Asad Ismail
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  3. Ben Y. Zhao
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  4. Sabrina Gaito
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  5. Gian Paolo Rossi
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  6. Haitao Zheng
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Correspondence to Ana Nika.

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Nika, A., Ismail, A., Zhao, B.Y. et al. Understanding and Predicting Data Hotspots in Cellular Networks. Mobile Netw Appl 21, 402–413 (2016). https://doi.org/10.1007/s11036-015-0648-6

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  • DOI: https://doi.org/10.1007/s11036-015-0648-6

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