Recently, stochastic geometry models for wireless communication networks have been attracting much attention. This is because the performance of such networks critically depends on the spatial configuration of wireless nodes and the irregularity of node configuration in a real network can be captured by a spatial point process. However, most analyses of such stochastic geometry models for wireless networks assume, due to its tractability, that the wireless nodes are located according to homogeneous Poisson point processes. This means that the wireless nodes are located independently with each other and their spatial correlation is ignored. In this work, we propose a stochastic geometry model of cellular networks such that the wireless base stations are located according to the Ginibre point process. The Ginibre point process is one of the determinantal point processes and accounts for the repulsion between the base stations. For the proposed model, we derive a computable representation for the coverage probability-the probability that the signal-to-interference-plus-noise ratio (SINR) for a mobile user achieves a target threshold. To capture its qualitative property, we further investigate the asymptotics of coverage probability as the SINR threshold becomes large in a special case. The results of numerical experiments are also exhibited.
The GPP, one of the main examples of determinantal point processes on the complex plane, has recently been proposed as a model for cellular networks in REF .
A cellular network model with Ginibre configurated base stations
Mathematics