Large deviations of the interference in the Ginibre network model

TL;DR

This paper derives large deviation estimates for interference in a wireless network modeled by the $eta$-Ginibre process, revealing different tail behaviors depending on fading distributions and network correlation levels.

Contribution

It provides the first large deviation analysis of interference in $eta$-Ginibre networks, highlighting how tail behaviors vary with fading types and node repulsiveness.

Findings

For bounded or Weibull superexponential fading, interference tails are influenced by multiple nearby nodes.

For exponential or subexponential fading, a single dominant interferer causes large interference, similar to Poisson networks.

Interference tail behavior converges to Poisson case under certain fading conditions, regardless of node repulsiveness.

Abstract

Under different assumptions on the distribution of the fading random variables, we derive large deviation estimates for the tail of the interference in a wireless network model whose nodes are placed, over a bounded region of the plane, according to the $\beta$-Ginibre process, $0<\beta\leq 1$. The family of $\beta$-Ginibre processes is formed by determinantal point processes, with different degree of repulsiveness, which converge in law to a homogeneous Poisson process, as $\beta \to 0$. In this sense the Poisson network model may be considered as the limiting uncorrelated case of the $\beta$-Ginibre network model. Our results indicate the existence of two different regimes. When the fading random variables are bounded or Weibull superexponential, large values of the interference are typically originated by the sum of several equivalent interfering contributions due to nodes in the vicinity of the receiver. In this case, the tail of the interference has, on the log-scale, the same asymptotic behavior for any value of $0<\beta\le 1$, but it differs (again on a log-scale) from the asymptotic behavior of the tail of the interference in the Poisson network model. When the fading random variables are exponential or subexponential, instead, large values of the interference are typically originated by a single dominating interferer node and, on the log-scale, the asymptotic behavior of the tail of the interference is essentially insensitive to the distribution of the nodes. As a consequence, on the log-scale, the asymptotic behavior of the tail of the interference in any $\beta$-Ginibre network model, $0<\beta\le 1$, is the same as in the Poisson network model.