Large-Scale Cellular Network Modeling From Population Data: An Empirical Analysis

Accurate estimates of the spatial distribution of base stations (BSs) are crucial for the analysis of core performance and connectivity metrics in cellular networks. The appropriate tuning of these models, particularly through the fitting of point processes (PPs), enables practical simulations and theoretical analyses with realistic network configurations. However, the commonly made assumption of stationarity in model parameters does not hold for larger networks, with especially population inhomogeneities causing large network structure variations. We use BS location information from different operators in Germany, to explore the efficacy and limitations of using population data as a covariate to approximate cellular deployments. Our analysis shows that the overall network density is highly correlated already for small areas. Furthermore, we find that for moderately populated areas, purely population-driven PPs yield a good statistical match to our real data. The validity boundaries discussed in this letter provide a useful reference for determining at which length scales, more complex, interaction-based, PPs are necessary.