Stronger wireless signals appear more Poisson

TL;DR

This paper demonstrates that the strongest wireless signals, after random propagation effects, can be modeled as a Poisson process, providing a practical application of theoretical convergence results in wireless signal analysis.

Contribution

It applies the main results of Keeler, Ross, and Xia (2016) in a simplified form to show that the strongest signals behave like a Poisson process, supporting recent experimental findings.

Findings

Strong signals, after propagation effects, follow a Poisson process.

The results support recent experimental observations.

Provides a practical application of theoretical convergence results.

Abstract

Keeler, Ross and Xia (2016) recently derived approximation and convergence results, which imply that the point process formed from the signal strengths received by an observer in a wireless network under a general statistical propagation model can be modelled by an inhomogeneous Poisson point process on the positive real line. The basic requirement for the results to apply is that there must be a large number of transmitters with different locations and random propagation effects.The aim of this note is to apply some of the main results of Keeler, Ross and Xia (2016) in a less general but more easily applicable form to illustrate how the results can be applied in practice. New results are derived that show that it is the strongest signals, after being weakened by random propagation effects, that behave like a Poisson process, which supports recent experimental work.