Analytical performance model for Poisson wireless networks with pathloss and shadowing propagation

Abstract

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.

SINR Model With Best Server Association for High Availability Studies of Wireless Networks

“…In [6], Mühleisen et al present an analysis of the SINR distribution for the Long Term Evolution (LTE) uplink, but it is restricted to only two links. Furthermore, in [7], Kelif et al numerically evaluate the impact of the best server association and show that in certain scenarios it is sufficient to neglect shadowing and connect the users to the closest BS. Another related work can be found in [8], where an approximation of the SINR distribution for hexagonal cellular networks is presented.…”