Mobility Prediction Based Resource Management

Imran M. A., Öztürk M., Abubakar A. I., Klaine P. V., Hussain S., Abbasi Q. H.

in: Wiley 5G Ref: The Essential 5G Reference Online, Rahim Tafazolli,Periklis Chatzimisios,Chin-Liang Wang, Editor, John Wiley & Sons, West Sussex, UK , London, pp.1-18, 2020

  • Publication Type: Book Chapter / Chapter Research Book
  • Publication Date: 2020
  • Publisher: John Wiley & Sons, West Sussex, UK 
  • City: London
  • Page Numbers: pp.1-18
  • Editors: Rahim Tafazolli,Periklis Chatzimisios,Chin-Liang Wang, Editor
  • Ankara Yıldırım Beyazıt University Affiliated: Yes


Owing to the challenging nature of emerging applications, such as augmented reality, 4 K video streaming, and remote surgery, 5G networks are expected to deal with a broad range of issues including higher data rate provision, less energy consumption, lower latency, etc.

Therefore, intelligent and proactive network management is envisioned to be an integral part of 5G in order to make the communication networks more agile, dynamic, and efficient. In this regard, mobility prediction has gained a significant amount of attention owing to their diverse application domains.

This article focuses on discussing the implementation of mobility prediction for resource management in mobile communication networks. After describing the challenges expected from 5G networks with related statistical facts, predictive mobility management is introduced with its distinctive applications.

After that, the use of mobility prediction in communication networks is elaborated according to different resource types, such as radio, energy, and built‐in resources, and the corresponding state‐of‐the‐art literature is classified accordingly.