Analytical Performance Model for Poisson Wireless Networks with Pathloss and Shadowing Propagation

TL;DR

This paper develops an analytical model to evaluate the SINR distribution in Poisson wireless networks, simplifying analysis by neglecting shadowing effects under certain conditions, and using a Fluid network model for quick performance assessment.

Contribution

It introduces a simplified analytical method for SINR evaluation in Poisson networks, neglecting shadowing when users connect to the strongest base station, and applies a Fluid model for efficient analysis.

Findings

Shadowing can often be neglected for SINR calculation when connecting to the best base station.

The proposed Fluid network model accurately analyzes stochastic Poisson networks.

Analytical expressions enable quick performance and QoS evaluation.

Abstract

The SINR (signal to interference plus noise ratio) is a key factor for wireless networks analysis. Indeed, the SINR distribution allows the derivation of performance and quality of service (QoS) evaluation. Moreover, it also enables the analysis of radio resources allocation and scheduling policies, since they depend on the SINR reached by a UE (User Equipment). Therefore, it is particularly interesting to develop an analytical method which allows to evaluate the SINR, in a simple and quick way, for a realistic environment. Considering a stochastic Poisson network model, we establish the CDF (cumulative distributed function) of the SINR. We show that the shadowing can be neglected, in many cases, as long as mobiles are connected to their best serving base station (BS), i.e. the BS which offers them the most powerful useful signal. As a consequence, the analysis of performance and quality of service, directly derived from the CDF of SINR, can be established by using a propagation model which takes into account only the pathloss. Moreover, we establish that the Fluid network model we have proposed can be used to analyze stochastic Poisson distributed network. Therefore, the analysis of stochastic Poisson network can be done in an easy and quick way, by using the analytical expression of the SINR established thanks to the Fluid network model.