A Flexible Dynamic Traffic Model for Reverse Link CDMA Cellular Networks

Ashtiani, Farid; Salehi, Jawad A.; Aref, Mohammad Reza

doi:10.1109/twc.2003.821197

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…Andersen and Nielsen presented in a simple Markovian framework for modeling local area network packet traffic with variability over several time scales and a fitting procedure for matching second‐order properties of counts to that of a second‐order self‐similar process. Ashtiani, Salehi, and Aref proposed in a flexible traffic model, which incorporated specific features for code division multiple access systems. Yang, Yan, and Hung proposed in a novel semi‐Markov process to model the Universal Mobile Telecommunications System discontinuous reception with bursty packet data traffic and provided inactivity timer and discontinuous reception cycle value selection guidelines for various packet traffic patterns.…”

Section: Introductionmentioning

confidence: 99%

A modified poisson distribution for smartphone background traffic in cellular networks

Zhang

Zhao

Guan

et al. 2016

Int J Communication

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SUMMARYFor the emerging applications such as Google Talk, Facebook, Skype and QQ, to mention a few, which run on smartphones, background traffic has become one of the significant issues in system design and optimization. Because of the complicated user behavior and interaction, the assumptions underlying the Poisson process model cannot be met; the Poisson distribution cannot approximate the distribution of background traffic arrivals accurately. In this paper, we propose a model, which can better fit the background traffic arrivals of smartphones than the Poisson distribution. The proposed model is a linear transformation of the Poisson distribution and is specified by three parameters, .a; b; /, which can be estimated from the measured sample's mean, variance, and third central moment. Simulation results have corroborated the fitness of the proposed model in both single and mixed applications scenarios. In addition, we have also observed that the normalized parameters, .a; b 0 ; 0 /, of each application is independent of the user number and completely characterized by the type of application. Hence, with the given trace cumulative distribution functions of all applications, the proposed modified Poisson distribution can be used as a tool for modeling and analyzing background traffic arrivals with arbitrary user numbers and mixed applications.

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Section: Introductionmentioning

confidence: 99%