Downlink Capacity and Base Station Density in Cellular Networks

TL;DR

This paper models cellular network performance using stochastic geometry, deriving user outage probabilities and success transmission densities, revealing diminishing returns in capacity with increased base station density.

Contribution

It introduces a stochastic geometry framework for analyzing downlink capacity and user outage in cellular networks, accounting for user density and interference.

Findings

Success transmission density increases with base station density

Diminishing returns in capacity gains with more base stations

Framework reduces need for extensive network simulations

Abstract

There have been a bulk of analytic results about the performance of cellular networks where base stations are regularly located on a hexagonal or square lattice. This regular model cannot reflect the reality, and tends to overestimate the network performance. Moreover, tractable analysis can be performed only for a fixed location user (e.g., cell center or edge user). In this paper, we use the stochastic geometry approach, where base stations can be modeled as a homogeneous Poisson point process. We also consider the user density, and derive the user outage probability that an arbitrary user is under outage owing to low signal-to-interference-plus-noise ratio or high congestion by multiple users. Using the result, we calculate the density of success transmissions in the downlink cellular network. An interesting observation is that the success transmission density increases with the base station density, but the increasing rate diminishes. This means that the number of base stations installed should be more than $n$-times to increase the network capacity by a factor of $n$. Our results will provide a framework for performance analysis of the wireless infrastructure with a high density of access points, which will significantly reduce the burden of network-level simulations.