A Mobility Model and Performance Analysis in Wireless Cellular Network with General Distribution and Multi-Cell Model

Abstract

Multi-cell mobility model and performance analysis for wireless cellular networks are presented. The mobility model plays an important role in characterizing different mobility-related parameters such as handoff call arrival rate, blocking or dropping probability, and channel holding time. We present a novel tractable multi-cell mobility model for wireless cellular networks under the general assumptions that the cell dwell times induced by mobiles' mobility and call holding times are modeled by using a general distribution instead of exponential distribution. We propose a novel generalized closed-form matrix formula to support the multi-cell mobility model and call holding time with general distributions. This allows us to develop a fixed point algorithm to compute loss probabilities, and handoff call arrival rate under the given assumptions. In order to reduce computational complexity of the fixed point algorithm, the channel holding time of each cell is down-modeled into an exponentially distributed one for purposes of simplification, since the service time is insensitive in computing loss probabilities of each cell due to Erlang insensitivity. The accuracy of the multi-cell analytic mobility model is supported by the comparison of the simulation results and the analytic ones.

A Markov Jump Process Model for Urban Vehicular Mobility: Modeling and Applications

“…The most closely related works are in theoretical analysis for cellular and WiFi networks [8]- [13], [34], [35]. In cellular network research, Markov mobility models have been used to analyze the performance around for decades from [9] to recent works of [12], [13].…”

“…In these works, the Markov model is used to predict user mobility in the cellular networks with the aims of resource reservation [10], [12], spectrum allocation [11] and performance evaluation [13]. For specific examples, Ashtiani et al [34] use a closed queueing network with a fixed number of nodes to model the users and traffic in the cellular network, Kim et al [35] utilise a M/M/c/c queues to mode cellular network mobile users, while Chen et al [8] propose a mixed queueing network model to describe the user mobility among access points in a campus wireless network environment. All these models for wireless networks are proposed under various assumptions to facilitate mathematical derivations.…”

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

“…Most of the previously published papers that have developed mathematical models for the performance analysis of mobile cellular systems considering general probability distribution for cell dwell time have either only presented numerical results for the Erlang (Wang & Fan, 2007;Rahman & Alfa, 2009;Kim & Choi, 2009) or Gamma distributions with shape parameter greater than one 1 (Yeo & Jun, 2002;, and/or only for the CHTh 2 (Fang, 2001;, or have not presented numerical results at all Alfa & Li, 2002;Soong & Barria, 2000). Thus, numerical results both for values of the coefficient of variation (CoV) of CDT greater than one and/or for the CHTn have been largely ignored.…”

“…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.…”

Call-Level Performance Sensitivity in Cellular Networks

“…The development of analytically tractable teletraffic models for performance evaluation of mobile cellular networks under more realistic assumptions has been the concern of recent works (Corral-Ruíz et al 2010;2005, Fang b;Kim & Choi 2009;Pattaramalai, 2009;Rico-Páez et al, 2007;Rodríguez-Estrello et al, 2009;Rodríguez-Estrello et al, 2010;Wang & Fan, 2007;Yeo & Yun, 2002;Zeng et al, 2002). The general conclusion of those works is that, in order to capture the overall effects of cellular shape, cellular size, users' mobility patterns, wireless channel unreliability, handoff schemes, and characteristics of new applications, most of the time interval variables (i.e., those used for modeling time duration of different events in telecommunications -for example, cell dwell time, residual cell dwell time, unencumbered interruption time, unencumbered service time) need to be modeled as random variables with general distributions.…”