Disruptive events in high-density cellular networks

TL;DR

This paper models and analyzes rare disruptive events in high-density cellular networks using a hybrid approach of ray launching simulations and stochastic geometry, revealing that increased density reduces the likelihood of large disconnection events.

Contribution

It introduces a new stochastic model combining ray launching data with point process theory to study atypical network disruptions in dense cellular environments.

Findings

Probability of large disconnection events decreases exponentially with higher transmitter density.

Dense networks exhibit increased stability against atypical user disconnection events.

Bottleneck effects are caused by users at the cell boundary and near-far interference effects.

Abstract

Stochastic geometry models are used to study wireless networks, particularly cellular phone networks, but most of the research focuses on the typical user, often ignoring atypical events, which can be highly disruptive and of interest to network operators. We examine atypical events when a unexpected large proportion of users are disconnected or connected by proposing a hybrid approach based on ray launching simulation and point process theory. This work is motivated by recent results using large deviations theory applied to the signal-to-interference ratio. This theory provides a tool for the stochastic analysis of atypical but disruptive events, particularly when the density of transmitters is high. For a section of a European city, we introduce a new stochastic model of a single network cell that uses ray launching data generated with the open source RaLaNS package, giving deterministic path loss values. We collect statistics on the fraction of (dis)connected users in the uplink, and observe that the probability of an unexpected large proportion of disconnected users decreases exponentially when the transmitter density increases. This observation implies that denser networks become more stable in the sense that the probability of the fraction of (dis)connected users deviating from its mean, is exponentially small. We also empirically obtain and illustrate the density of users for network configurations in the disruptive event, which highlights the fact that such bottleneck behaviour not only stems from too many users at the cell boundary, but also from the near-far effect of many users in the immediate vicinity of the base station. We discuss the implications of these findings and outline possible future research directions.