A modified poisson distribution for smartphone background traffic in cellular networks

Abstract

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.

Statistical Modelling of Dynamic Interference Threshold and Its Effect on Network Capacity

“…Ideally, one can use any of the available continuous or discrete distributions, but considering the close applicability of discrete distributions and usage for capacity demand modeling, Poisson distribution is found to be the best model to represent the PU capacity demand [28]- [32]. In addition, the insights provided in this study by modelling capacity demand by any of the available distributions will remain the same.…”

“…In this section, we will derive the interference power threshold from the variable traffic demand distribution considering a CRN system as described in the previous section 5 . First of all, the data traffic distribution or the variable capacity distribution is discrete in nature, and therefore has been modelled by different available discrete distribution's [28]- [32]. However, among those discrete distributions, Poisson distribution becomes a very strong candidate as it has been used in telecommunications since the advent of computer networks, and with proper selection of parameters can be made to fit to most network traffic models [33], [34].…”

Behavior predictability and consistency of mobile users' traffic usage between different years based on entropy theory

“…According to Huang and Salvador, the performance of a cellular network changes significantly as the combinations of traffic types in the network change. Zhang et al proposed a modified Poisson distribution that accurately approximate the distribution of background traffic arrivals, which is beneficial for optimization. In addition, using advanced knowledge of an individual's future traffic type, operators can predict traffic combinations in specified network regions and can thus take appropriate measures to optimize the performance of the network in the next moment, for instance, by adjusting the configuration of or deploying resources to the region …”