Spatiotemporal Mobile Data Traffic Modeling: The Case of UMTS Network in Addis Ababa, Ethiopia

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The exponential growth in mobile data traffic is forcing telecom operators to invest on new infrastructures. But devising techniques for optimum network utilization, which can be provided by traffic modeling, has the potential to reduce investment costs. Modeling traffic variation in different service areas and time can also be applicable for energy efficient network planning, understanding customers’ data traffic usage behavior and dynamic resource allocation. In this thesis, based on the data collected from 734 Base Stations located in Addis Ababa, Ethiopia, the real traffic is modeled in space and time with a tunable accuracy. Firstly, a rectangle that can inscribe the geographical area of the city is selected and divided equally in to N by M smaller groups. Secondly, to understand the temporal behavior in the data, the time-series data traffic of each group is transformed to spectral domain by using Fast Fourier Transform where it is observed that all groups have the same four major frequency components but with different magnitude of coefficients and phases. Then, matrices corresponding to coefficient and phase values are transformed from spatial to spectral domain by applying Two Dimensional Discrete Cosine Transform. In spectral domain higher frequency components that contain less information are removed out and the remaining are used for the reverse transform that made the model to be complete in time and space. The different application areas of the model for the operator require a varying level of accuracy which in turn is dependent on the truncation level of frequency components. As a result, a relation between model performance and truncation level is developed which formed the model as a tunable around accuracy. Finally, by defining parameters that use the model as an input, the network performance is analyzed and suggestions for the observed gaps are presented.



Spatio-temporal, Modeling, UMTS Data Traffic, Fast Fourier Transform, Two Dimensional Discrete Cosine Transform