A Tractable Approach to Coverage and Rate in Cellular Networks

TL;DR

This paper introduces a new stochastic geometry-based model for cellular network coverage and rate analysis that is both mathematically tractable and more representative of real-world deployments than traditional grid models.

Contribution

The authors develop a general, tractable stochastic geometry model for multi-cell SINR, providing explicit formulas for coverage probability and rate, and compare it with existing models and real deployments.

Findings

The new model offers lower-bound coverage estimates, being pessimistic but accurate.

The model simplifies to common integrals or closed-form expressions in special cases.

It better captures dense, opportunistic base station deployments in future networks.

Abstract

Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.