Measurement-Driven Modeling for Connection Density and Traffic Distribution in Large-Scale Urban Mobile Networks - 2018


In the varied usage scenarios of mobile networks, we have a tendency to have totally different performance needs on connection density and user experienced information rate, and modeling such diversity is crucial to the strategy evaluation in addressing the problem of high traffic load and scalability of network resources. Therefore, it's necessary to make a network capability model in two dimensions of affiliation density and user experienced information rate. This Project aims at addressing this challenge based mostly on an investigation of network capability in massive-scale urban environments. Initial, our statistical study shows that the spatial distribution of these two parameters can be accurately modelled by the log-normal mixture distribution. Second, we notice that only six basic capability patterns exist among the nine,00zero cellular base stations, which indicates different levels of network capabilities. A lot of importantly, these discoveries are similar in an exceedingly cellular network deployed in an exceedingly different town. Therefore, based mostly on these two discoveries, we build a network capability model which will generate synthetic base stations with various association density and user experienced data rate. We believe that this system of modeling network capability, with accuracy, generality, and suppleness, can help telecommunication operators to style and standardize mobile networks of the subsequent generation.

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