Modeling Heterogeneous Network Interference Using Poisson Point Processes

TL;DR

This paper develops a stochastic geometry-based model using Poisson point processes to analyze interference in heterogeneous cellular networks, enabling more accurate performance evaluation of communication strategies.

Contribution

It introduces a novel interference modeling approach that accounts for various interference sources and approximates the interference distribution with a Gamma distribution for simplified analysis.

Findings

Gamma approximation effectively models interference distribution

Model accurately predicts outage probability and data rates

Incorporates small-scale and large-scale fading effects

Abstract

Cellular systems are becoming more heterogeneous with the introduction of low power nodes including femtocells, relays, and distributed antennas. Unfortunately, the resulting interference environment is also becoming more complicated, making evaluation of different communication strategies challenging in both analysis and simulation. Leveraging recent applications of stochastic geometry to analyze cellular systems, this paper proposes to analyze downlink performance in a fixed-size cell, which is inscribed within a weighted Voronoi cell in a Poisson field of interferers. A nearest out-of-cell interferer, out-of-cell interferers outside a guard region, and cross-tier interference are included in the interference calculations. Bounding the interference power as a function of distance from the cell center, the total interference is characterized through its Laplace transform. An equivalent marked process is proposed for the out-of-cell interference under additional assumptions. To facilitate simplified calculations, the interference distribution is approximated using the Gamma distribution with second order moment matching. The Gamma approximation simplifies calculation of the success probability and average rate, incorporates small-scale and large-scale fading, and works with co-tier and cross-tier interference. Simulations show that the proposed model provides a flexible way to characterize outage probability and rate as a function of the distance to the cell edge.