  This paper aims to validate the $\beta$-Ginibre point process as a model for
the distribution of base station locations in a cellular network. The
$\beta$-Ginibre is a repulsive point process in which repulsion is controlled
by the $\beta$ parameter. When $\beta$ tends to zero, the point process
converges in law towards a Poisson point process. If $\beta$ equals to one it
becomes a Ginibre point process. Simulations on real data collected in Paris
(France) show that base station locations can be fitted with a $\beta$-Ginibre
point process. Moreover we prove that their superposition tends to a Poisson
point process as it can be seen from real data. Qualitative interpretations on
deployment strategies are derived from the model fitting of the raw data.
