  The spatial structure of transmitters in wireless networks plays a key role
in evaluating the mutual interference and hence the performance. Although the
Poisson point process (PPP) has been widely used to model the spatial
configuration of wireless networks, it is not suitable for networks with
repulsion. The Ginibre point process (GPP) is one of the main examples of
determinantal point processes that can be used to model random phenomena where
repulsion is observed. Considering the accuracy, tractability and
practicability tradeoffs, we introduce and promote the $\beta$-GPP, an
intermediate class between the PPP and the GPP, as a model for wireless
networks when the nodes exhibit repulsion. To show that the model leads to
analytically tractable results in several cases of interest, we derive the mean
and variance of the interference using two different approaches: the Palm
measure approach and the reduced second moment approach, and then provide
approximations of the interference distribution by three known probability
density functions. Besides, to show that the model is relevant for cellular
systems, we derive the coverage probability of the typical user and also find
that the fitted $\beta$-GPP can closely model the deployment of actual base
stations in terms of the coverage probability and other statistics.
