@techreport{TD:101015,
	att_abstract={{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 {em 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 {em leap graph}, where an
edge (or a {em 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.}},
	att_authors={zg2325, sl1858, jp935w, nd1321},
	att_categories={C_NSS.7},
	att_copyright={{Springer}},
	att_copyright_notice={{The definitive version was published in  2012. {{, 2013-03-18}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={handover data,  mobility prediction,  leap graph},
	att_techdoc={true},
	att_techdoc_key={TD:101015},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101015_DS1_2012-10-15T20:41:26.114Z.pdf},
	author={Zihui Ge and Seungjoon Lee and Jeffrey Pang and Nicholas Duffield and Wei Dong},
	institution={{14th Passive and Active Measurement Conference}},
	month={March},
	title={{Modeling Cellular User Mobility using a Leap Graph}},
	year=2013,
}