The volume and types of traffic data in mobile cellular networks have beenincreasing continuously. Meanwhile, traffic data change dynamically in severaldimensions such as time and space. Thus, traffic modeling is essential fortheoretical analysis and energy efficient design of future ultra-dense cellularnetworks. In this paper, the authors try to build a tractable and accuratemodel to describe the traffic variation pattern for a single base station inreal cellular networks. Firstly a sinusoid superposition model is proposed fordescribing the temporal traffic variation of multiple base stations based onreal data in a current cellular network. It shows that the mean traffic volumeof many base stations in an area changes periodically and has three mainfrequency components. Then, lognormal distribution is verified for spatialmodeling of real traffic data. The spatial traffic distributions at both sparetime and busy time are analyzed. Moreover, the parameters of the model arepresented in three typical regions: park, campus and central business district.Finally, an approach for combined spatial-temporal traffic modeling of singlebase station is proposed based on the temporal and spatial traffic distributionof multiple base stations. All the three models are evaluated throughcomparison with real data in current cellular networks. The results show thatthese models can accurately describe the variation pattern of real traffic datain cellular networks.
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