Enriched K-Tier HetNet Model to Enable the Analysis of User-Centric Small Cell Deployments

TL;DR

This paper introduces an enriched HetNet model that accounts for user clustering around small cell base stations, providing more accurate coverage analysis for user-centric deployments.

Contribution

It extends the K-tier Poisson model by incorporating Poisson Cluster Processes for user locations, enabling analysis of correlated user and BS distributions.

Findings

Coverage probability decreases with larger user clusters.

Results unify clustered and uniform user distributions.

Closed-form bounds for specific cluster models are derived.

Abstract

One of the principal underlying assumptions of current approaches to the analysis of heterogeneous cellular networks (HetNets) with random spatial models is the uniform distribution of users independent of the base station (BS) locations. This assumption is not quite accurate, especially for user-centric capacity-driven small cell deployments where low-power BSs are deployed in the areas of high user density, thus inducing a natural correlation in the BS and user locations. In order to capture this correlation, we enrich the existing K-tier Poisson Point Process (PPP) HetNet model by considering user locations as Poisson Cluster Process (PCP) with the BSs at the cluster centers. In particular, we provide the formal analysis of the downlink coverage probability in terms of a general density functions describing the locations of users around the BSs. The derived results are specialized for two cases of interest: (i) Thomas cluster process, where the locations of the users around BSs are Gaussian distributed, and (ii) Mat\'ern cluster process, where the users are uniformly distributed inside a disc of a given radius. Tight closed-form bounds for the coverage probability in these two cases are also derived. Our results demonstrate that the coverage probability decreases as the size of user clusters around BSs increases, ultimately collapsing to the result obtained under the assumption of PPP distribution of users independent of the BS locations when the cluster size goes to infinity. Using these results, we also handle mixed user distributions consisting of two types of users: (i) uniformly distributed, and (ii) clustered around certain tiers.