Performance laws of large heterogeneous cellular networks

TL;DR

This paper develops a comprehensive model for heterogeneous cellular networks to derive macroscopic performance laws, validated with real field data, considering user arrivals, base station types, interference, and signal quality.

Contribution

It introduces a novel stochastic model capturing the spatial and temporal dynamics of heterogeneous cellular networks, enabling analysis of performance laws involving network geometry and interference.

Findings

Derived macroscopic performance laws validated with real data

Quantified impact of interference on user throughput

Provided insights into network load and quality of service

Abstract

We propose a model for heterogeneous cellular networks assuming a space-time Poisson process of call arrivals, independently marked by data volumes, and served by different types of base stations (having different transmission powers) represented by the superposition of independent Poisson processes on the plane. Each station applies a processor sharing policy to serve users arriving in its vicinity, modeled by the Voronoi cell perturbed by some random signal propagation effects (shadowing). Users' peak service rates depend on their signal-to-interference-and-noise ratios (SINR) with respect to the serving station. The mutual-dependence of the cells (due to the extra-cell interference) is captured via some system of cell-load equations impacting the spatial distribution of the SINR. We use this model to study in a semi-analytic way (involving only static simulations, with the temporal evolution handled by the queuing theoretic results) network performance metrics (cell loads, mean number of users) and the quality of service perceived by the users (mean throughput) served by different types of base stations. Our goal is to identify macroscopic laws regarding these performance metrics, involving averaging both over time and the network geometry. The reveled laws are validated against real field measurement in an operational network.