A Unified Framework for SINR Analysis in Poisson Networks with Traffic Dynamics

TL;DR

This paper introduces a comprehensive framework combining stochastic geometry and queueing theory to analyze SINR performance in Poisson wireless networks with traffic dynamics, accounting for dependencies among transmitters.

Contribution

It presents a novel analysis method that models interdependent active states of transmitters and non-homogeneous interferer distributions, improving accuracy over traditional models.

Findings

The framework accurately predicts transmission success probability.

It characterizes the SINR meta distribution considering traffic dynamics.

Guidelines for optimal network deployment are derived.

Abstract

We study the performance of wireless links for a class of Poisson networks, in which packets arrive at the transmitters following Bernoulli processes. By combining stochastic geometry with queueing theory, two fundamental measures are analyzed, namely the transmission success probability and the meta distribution of signal-to-interference-plus-noise ratio (SINR). Different from the conventional approaches that assume independent active states across the nodes and use homogeneous point processes to model the locations of interferers, our analysis accounts for the interdependency amongst active states of the transmitters in space and arrives at a non-homogeneous point process for the modeling of interferers' positions, which leads to a more accurate characterization of the SINR. The accuracy of the theoretical results is verified by simulations, and the developed framework is then used to devise design guidelines for the deployment strategies of wireless networks.