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Case study: Mobile networks

Bellahsene, Samir, Kloul, Leïla, Reinecke, Philipp and Wolter, Katinka 2012. Case study: Mobile networks. In: Wolter, K, Avritzer, A, Vieira, M and van Moorsel, A eds. Resilience Assessment and Evaluation of Computing Systems, Berlin, Heidelberg: Springer, pp. 343-364. (10.1007/978-3-642-29032-9_17)

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Abstract

In order to be resilient, a network must possess means to ensure connectivity even in the presence of disturbances. This chapter will study two different approaches to increase resilience of mobile networks, in the context of the handover procedure. Seamless handovers between base stations is a prerequisite for service continuity in mobile networks. The handover process consists in handing off a call to a new base station when the mobile user moves to its corresponding cell while the call is in progress. It imposes frequency synchronicity requirements which imply strict bounds on the tolerable frequency deviations of base-station clocks. The preferred protocol for frequency synchronisation in packet-switched backhaul networks is the Precision Time Protocol (PTP). In the first part of this chapter, the suitability of backhaul networks for accurate frequency synchronisation using PTP is investigated. Two solutions for improving accuracy are derived. While the first is applicable to networks of any topology, but may require costly reconfiguration, the second is limited to specific setups, but can be applied without changing the network. The second part of this chapter is dedicated to the performance analysis of a Markov-based prediction model. Mobility prediction constitute an important solution to enable seamless handovers in cellular networks. The mobility trace is the main information used to perform mobility prediction. However, using solely this information makes the prediction process difficult when the mobile user is new in the network, that is, when its mobility trace is poor. The efficiency of the prediction model relies on both the ability of the model to predict successfully the next move of a mobile user and its ability to perform such a prediction in a short delay. In order to assess the Markov-based prediction model, data sets of a real cellular network in a major US urban area are used.

Item Type: Book Section
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-642-29031-2
Last Modified: 11 Sep 2019 13:15
URI: http://orca.cf.ac.uk/id/eprint/124318

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