Modeling and Performance Analysis of Beyond 3G Integrated Wireless Networks

Next-generation wireless networking is evolving towards a multi-service heterogeneous paradigm that converges different pervasive access technologies and provides a large set of novel revenue generating applications. Hence, system complexity increases due to its embedded heterogeneity, which can not be accounted by the existing modeling and performance evaluation techniques. Consequently, the development of new modeling approaches becomes as a crucial requirement for proper system design and performance evaluation. This paper presents a novel mobility model for a two-tier integrated wireless system using a new modeling approach that accommodates the aforementioned complexity. Additionally, a novel session model is developed as an adapted version of the proposed mobility model. These models use phase-type distributions that are known to approximate any generic probability laws. Using the proposed session model, a novel generic analytical framework is developed to obtain several salient performance metrics such as network utilization times and handoff rates. Simulation and analysis results prove the proposed model validity and demonstrate the accuracy of the novel modeling approach when compared with traditional modeling techniques.

[1]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

[2]  Zygmunt J. Haas,et al.  Predictive distance-based mobility management for multidimensional PCS networks , 2003, TNET.

[3]  Seung-Jae Han,et al.  Integration of 802.11 and third-generation wireless data networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[4]  Yuguang Fang,et al.  Channel Occupancy Times and Handoff Rate for Mobile Computing and PCS Networks , 1998, IEEE Trans. Computers.

[5]  Ren Asmussen,et al.  Fitting Phase-type Distributions via the EM Algorithm , 1996 .

[6]  Vaidyanathan Ramaswami,et al.  Introduction to Matrix Analytic Methods in Stochastic Modeling , 1999, ASA-SIAM Series on Statistics and Applied Mathematics.

[7]  Myron Hlynka,et al.  Queueing Networks and Markov Chains (Modeling and Performance Evaluation With Computer Science Applications) , 2007, Technometrics.

[8]  Nigel G. Bean,et al.  Characterization of Matrix-Exponential Distributions , 2008 .

[9]  Gunter Bolch,et al.  Queueing Networks and Markov Chains - Modeling and Performance Evaluation with Computer Science Applications, Second Edition , 1998 .

[10]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

[11]  Attahiru Sule Alfa,et al.  A Homogeneous PCS network with Markov Call Arrival Process and Phase Type Cell Residence Time , 2002, Wirel. Networks.

[12]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[13]  Randy H. Katz,et al.  Vertical handoffs in wireless overlay networks , 1998, Mob. Networks Appl..

[14]  Apostolis K. Salkintzis,et al.  WLAN-GPRS integration for next-generation mobile data networks , 2002, IEEE Wirel. Commun..

[15]  Ahmed H. Zahran,et al.  Signal threshold adaptation for vertical handoff in heterogeneous wireless networks , 2006, Mob. Networks Appl..

[16]  Yuguang Fang,et al.  Modeling PCS networks under general call holding time and cell residence time distributions , 1997, TNET.

[17]  Yuguang Fang,et al.  Call Performance for a PCS Network , 1997, IEEE J. Sel. Areas Commun..

[18]  Stephen S. Rappaport,et al.  A model for teletraffic performance and channel holding time characterization in wireless cellular communication with general session and dwell time distributions , 1998, IEEE J. Sel. Areas Commun..

[19]  LiangBen,et al.  Predictive distance-based mobility management for multidimensional PCS networks , 2003 .