Analysis of Interference Correlation in Non-Poisson Networks

TL;DR

This paper quantifies interference correlation in non-Poisson wireless networks with clustered interferers, revealing that clustering increases correlation and depends on cluster size and radius, with implications validated by simulations.

Contribution

It provides the first analytical quantification of interference correlation in non-Poisson clustered networks, highlighting the impact of clustering parameters.

Findings

Clustering increases interference correlation compared to Poisson networks.

Correlation rises with the average number of points per cluster.

Correlation decreases as cluster radius increases.

Abstract

The correlation of interference has been well quantified in Poisson networks where the interferers are independent of each other. However, there exists dependence among the base stations (BSs) in wireless networks. In view of this, we quantify the interference correlation in non-Poisson networks where the interferers are distributed as a Matern cluster process (MCP) and a second-order cluster process (SOCP). Interestingly, it is found that the correlation coefficient of interference for the Matern cluster networks, $\zeta_{MCP}$, is equal to that for second-order cluster networks, $\zeta_{SOCP}$. Furthermore, they are greater than their counterpart for the Poisson networks. This shows that clustering in interferers enhances the interference correlation. In addition, we show that the correlation coefficients $\zeta_{MCP}$ and $\zeta_{SOCP}$ increase as the average number of points in each cluster, $c$, grows, but decrease with the increase in the cluster radius, $R$. More importantly, we point that the effects of clustering on interference correlation can be neglected as $\frac{c}{\pi^{2}R^{2}}\rightarrow0$. Finally, the analytical results are validated by simulations.