Uplink Interference Analysis for Two-tier Cellular Networks with Diverse Users under Random Spatial Patterns

TL;DR

This paper develops a stochastic geometric model to analyze uplink interference in two-tier cellular networks with diverse user distributions, considering heterogeneity and exclusion regions, and applies it to optimize network income.

Contribution

It introduces a novel analytical framework for uplink interference in heterogeneous networks with complex spatial user distributions and exclusion zones.

Findings

Interference analysis can be formulated as a convex optimization problem.

The model accurately predicts interference and outage probabilities.

Numerical results validate the analytical approach.

Abstract

Multi-tier architecture improves the spatial reuse of radio spectrum in cellular networks, but it introduces complicated heterogeneity in the spatial distribution of transmitters, which brings new challenges in interference analysis. In this work, we present a stochastic geometric model to evaluate the uplink interference in a two-tier network considering multi-type users and base stations. Each type of tier-1 users and tier-2 base stations are modeled as independent homogeneous Poisson point processes, and tier-2 users are modeled as locally non-homogeneous clustered Poisson point processes centered at tier-2 base stations. By applying a superposition-aggregation-superposition approach, we quantify the interference at both tiers. Our model is also able to capture the impact of two types of exclusion regions, where either tier-2 base stations or tier-2 users are restricted in order to avoid cross-tier interference. As an important application of this analytical model, an intensity planning scenario is investigated, in which we aim to maximize the total income of the network operator with respect to the intensities of tier-2 cells, under constraints on the outage probabilities of tier-1 and tier-2 users. The result of our interference analysis suggests that this maximization can be converted to a standard convex optimization problem. Finally, numerical studies further demonstrate the correctness of our analysis.