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

In the diverse usage scenarios of mobile networks, we have different performance requirements on connection density and user experienced data 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 is necessary to build a network capability model in two dimensions of connection density and user experienced data rate. This paper aims at addressing this challenge based on an investigation of network capability in large-scale urban environments. First, our statistical study shows that the spatial distribution of these two parameters can be accurately modelled by the log-normal mixture distribution. Second, we find that only six basic capability patterns exist among the 9,000 cellular base stations, which indicates different levels of network capabilities. More importantly, these discoveries are similar in a cellular network deployed in a different city. Therefore, based on these two discoveries, we build a network capability model that can generate synthetic base stations with diverse connection density and user experienced data rate. We believe that this methodology of modeling network capability, with accuracy, generality, and flexibility, can help telecommunication operators to design and standardize mobile networks of the next generation.