A Modified Poisson Distribution For Smartphone Background Traffic In Cellular Networks

For 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, lambda), 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), lambda(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. Copyright (C) 2016 John Wiley & Sons, Ltd.