Effect of cell residence time distribution on the performance of cellular mobile networks

A user's profile, for the purpose of location management in a personal communication service (PCS) network, is formalized as a subgraph of the network graph. This subgraph, the so-called individual profile graph (IPG), is determined after a period of observation with the intent of predicting and codifying the user's diurnal routine. The IPG is easily-motivated, robust, straightforwardly computed from observed data, and, under fairly intuitive assumptions, provably predictive of the user's diurnal routine. An IPG-based paging and update strategy is analyzed. It is shown to significantly improve a straight location area (LA) based strategy that ignores user profiles.

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“…is the unique maximum of terms in Sequence (5). Then, from the definition of G U in Equation (6) it is seen that, indeed, G U = G U * .…”

Section: Corollary 2 With the Assumptions (A)-(d) Above If δ And δ Amentioning

confidence: 94%

“…The size of a block B is the number of slots comprising it. We'll assume that each interval of time spent by U in any cell v follows an exponential distribution, which is a common assumption [5]. As the exponential distribution is continuous, in subsequent calculations we'll discretize it over the integral block size.…”

Section: Validitymentioning

confidence: 99%

Individual profile graphs for location management in PCS networks

Chuon

Guha

2008

Computer Communications

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“…In specific, the use of general distributions for modeling this time variable has been highlighted. In this research direction, some authors have used Erlang, gamma, uniform, deterministic, hyper-Erlang, sum of hyper-exponentials, log-normal, Pareto, and Weibull distributions to model the pdf of CDT; see (Wang & Fan, 2007;Fang, 2001Rahman & Alfa, 2009;Pattaramalai et al, 2007Pattaramalai et al, , 2009Hidata et al, 2002;Thajchayapong & Toguz, 2005;Khan & Zeghlache, 1997;Zeng et al 2002;Kim & Choi, 2009) and the references therein. Fang in (Fang, 2001)) emphasizes the use of phase-type (PH) distributions for modeling CDT.…”

Section: Introductionmentioning

confidence: 99%

Cell Dwell Time and Channel Holding Time Relationship in Mobile Cellular Networks

Corral-Ruiz¹,

Cruz-Perez²,

Hernandez-Valdez³

2012

Wireless Communications and Networks - Recent Advances

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“…The telecommunication system consists of limited transmission channels or capacity, use to serve some volume of subscribers at a particular point in time. The manner at which subscribers uses these limited channels are random in nature, therefore these transmission channels are used or served in random process (Chi Wa et al, 2004;Hong et al,1986 andKhan et al,1997). Random process is one in which one or more quantities vary with time in such a way that the instantaneous values of quantities are not determined precisely but are predictable with certain probability, these quantities are known as random variables (Sanjay, 2010).…”

Section: Introductionmentioning

confidence: 99%

Elusive Statistical Property of Arrival Rate and Holding Time used in Mobile Communication Networks

A¹,

O²,

Emagbetere³

2012

IJCA

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This research work is aimed at the study of arrival rate and holding time used in mobile communication networks, also to determine the best suitable statistical probability distribution of both arrival rate and holding time or service time in mobile communication network. The most general acceptable assumption about arrival rate is Poisson distribution and the holding time is exponential distribution in traffic modeling of mobile communication networks. Exhaustive literature review is deployed for details explanation on discrete random variables of arrival rate and continuous holding time use in traffic modeling of mobile communication networks. From the research work, the arrival rate is explained using point process or counting process, which leads to two unique properties, they are orderly and memorylessness. These unique properties are possessed by Bernoulli process with is discrete time, having Geometric distribution function, also with Poisson process, which is continuous time and discrete space, having Exponential distribution function which is used to characterize arrival rate based on interarrival rate process. Therefore, from the research work, it is assumed that arrivals rate is Poisson distribution and service time or holding time is exponentially distributed in traffic situation in mobile communication networks. These statistical properties since to the best suitable in mobile communication networks because of their unique parameters and are simple to analyses.

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Effect of cell residence time distribution on the performance of cellular mobile networks

Cited by 64 publications

References 8 publications

Individual profile graphs for location management in PCS networks

Individual profile graphs for location management in PCS networks

Cell Dwell Time and Channel Holding Time Relationship in Mobile Cellular Networks

Elusive Statistical Property of Arrival Rate and Holding Time used in Mobile Communication Networks

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