Channel holding time in mobile cellular networks with heavy-tailed distributed cell dwell time

Channel holding time is fundamental for the performance analysis/evaluation of mobile cellular networks. Channel holding time depends on both call holding time and cell dwell time. In the literature, many assumptions on cell dwell time distribution have been done and different channel holding time characteristics have been obtained. However, to our knowledge, channel holding time statistics has not been obtained under the assumption of heavy-tailed distributed cell dwell time. In this paper, this is addressed by considering Pareto, log-normal or Weibull distributed cell dwell time. Additionally, under the assumption that call holding time is exponentially distributed and cell dwell time is Pareto distributed, novel mathematical expressions for the probability distribution of the channel holding time for new and handed off calls are derived. Numerical results show the extent by which channel holding time statistics are affected by the different parameters of the cell dwell time distribution.

[1]  Jon M. Peha,et al.  Mobility Patterns in Microcellular Wireless Networks , 2006, IEEE Trans. Mob. Comput..

[2]  Djamal Zeghlache,et al.  Effect of cell residence time distribution on the performance of cellular mobile networks , 1997, 1997 IEEE 47th Vehicular Technology Conference. Technology in Motion.

[3]  Takehiko Kobayashi,et al.  Vehicle Motion in Large and Small Cities and Teletraffic Characterization in Cellular Communication Systems , 2000 .

[4]  Valentine A. Aalo,et al.  Call Completion Probability with Weibull Distributed Call Holding Time and Cell Dwell Time , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[5]  Valentine A. Aalo,et al.  Distribution of random sum cell dwell times in wireless network , 2008 .

[6]  Ozan K. Tonguz,et al.  Performance implications of Pareto-distributed cell residual time in distributed admission control scheme (DACS) , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[7]  Yi-Bing Lin,et al.  Queueing priority channel assignment strategies for PCS hand-off and initial access , 1994 .

[8]  T. Kobayashi,et al.  Statistical characteristics of pedestrians' motion and effects on teletraffic of mobile communication networks , 2005, Second IFIP International Conference on Wireless and Optical Communications Networks, 2005. WOCN 2005..

[9]  H. Hidaka.  Self-Similarity in Cell Dwell Time Caused by Terminal Motion and Its Effects on Teletraffic of Cellular Communication Networks , 2002 .

[10]  Yi-Bing Lin,et al.  Random number generation for excess life of mobile user residence time , 2006, IEEE Transactions on Vehicular Technology.

[11]  H. Boche,et al.  Dwell time models for wireless communication systems , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[12]  Takehiko Kobayashi,et al.  Vehicle Mobility Characterization Based on Measurements and Its Application to Cellular Communication Systems (Special Issue on Multimedia Mobile Communication Systems) , 1999 .

[13]  S. Wittevrongel,et al.  Queueing Systems , 2019, Introduction to Stochastic Processes and Simulation.

[14]  Felipe A. Cruz-Pérez,et al.  Cell Dwell Time and Channel Holding Time Relationship in Mobile Cellular Networks , 2012 .

[15]  Takehiko Kobayashi,et al.  Teletraffic Characteristics of Cellular Communication for Different Types of Vehicle Motion , 2001 .

[16]  V. Vaidehi,et al.  A Pragmatic Analysis of User Mobility Patterns in Macrocellular Wireless Networks , 2007, WOWMOM.