Downlink Performance Analysis for a Generalized Shotgun Cellular System

TL;DR

This paper provides a comprehensive analysis of downlink SINR performance in generalized shotgun cellular systems, revealing key invariances and effects of various network parameters through semi-analytical methods.

Contribution

It introduces a generalized model for cellular networks using Poisson point processes and derives semi-analytical SINR coverage expressions applicable to diverse scenarios.

Findings

SINR distribution is invariant to fading distribution in interference-limited homogeneous Poisson networks.

Coverage probability is independent of base station density under certain conditions.

The analysis techniques are applicable to cognitive radio, femtocell, and multi-tier networks.

Abstract

In this paper, we analyze the signal-to-interference-plus-noise ratio (SINR) performance at a mobile station (MS) in a random cellular network. The cellular network is formed by base-stations (BSs) placed in a one, two or three dimensional space according to a possibly non-homogeneous Poisson point process, which is a generalization of the so-called shotgun cellular system. We develop a sequence of equivalence relations for the SCSs and use them to derive semi-analytical expressions for the coverage probability at the MS when the transmissions from each BS may be affected by random fading with arbitrary distributions as well as attenuation following arbitrary path-loss models. For homogeneous Poisson point processes in the interference-limited case with power-law path-loss model, we show that the SINR distribution is the same for all fading distributions and is not a function of the base station density. In addition, the influence of random transmission powers, power control, multiple channel reuse groups on the downlink performance are also discussed. The techniques developed for the analysis of SINR have applications beyond cellular networks and can be used in similar studies for cognitive radio networks, femtocell networks and other heterogeneous and multi-tier networks.