An Analytical Framework for Modeling a Spatially Repulsive Cellular Network

TL;DR

This paper introduces a hybrid cellular network model combining deterministic grid and random Poisson deployments, accurately predicting user association and coverage probability based on deployment data.

Contribution

It presents a novel analytical framework modeling base stations as a superposition of grid and Poisson processes, bridging the gap between idealized and real-world deployments.

Findings

Coverage probability depends on the intensity ratio of grid to Poisson base stations.

User association is biased towards grid base stations.

Model accurately predicts coverage in actual deployments.

Abstract

We propose a new cellular network model that captures both deterministic and random aspects of base station deployments. Namely, the base station locations are modeled as the superposition of two independent stationary point processes: a random shifted grid with intensity $\lambda_g$ and a Poisson point process (PPP) with intensity $\lambda_p$. Grid and PPP deployments are special cases with $\lambda_p \to 0$ and $\lambda_g \to 0$, with actual deployments in between these two extremes, as we demonstrate with deployment data. Assuming that each user is associated with the base station that provides the strongest average received signal power, we obtain the probability that a typical user is associated with either a grid or PPP base station. Assuming Rayleigh fading channels, we derive the expression for the coverage probability of the typical user, resulting in the following observations. First, the association and the coverage probability of the typical user are fully characterized as functions of intensity ratio $\rho_\lambda = \lambda_p/\lambda_g$. Second, the user association is biased towards the base stations located on a grid. Finally, the proposed model predicts the coverage probability of the actual deployment with great accuracy.