Impact of transmitting power on femto cells performance and coverage in heterogeneous wireless networks

2014 International Conference and Workshop on the Network of the Future (NOF)

Sénécal

Bridon

et al. 2014

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The evaluation of performance and quality of service needs the modeling of wireless networks. Among the different models usually considered, the hexagonal network is the most popular. However, it requires extensive numerical computations. The Poisson network model, for which the base stations (BS) locations form a stochastic spatial Poisson process, allows to consider a non constant distance between base stations. Therefore, it characterizes more realistically operational networks. The Fluid network model, for which the interfering BS are replaced by a continuum of infinitesimal interferers, allows to establish closed-form formula for the SINR (Signal on Interference plus Noise Ratio). This model was validated by comparison with an hexagonal network. The two models establish very close results. In this paper, we show that the Fluid network model can also be used to analyze Poisson networks. Therefore, the evaluation of quality of service and performance becomes very easy, whatever the type of model, by using the analytical expression of the SINR established by considering the fluid model.

Section: Calculation Of the Sinrmentioning

confidence: 77%

Quality of service and performance evaluation: A fluid approach for poisson wireless networks

2014 International Conference and Workshop on the Network of the Future (NOF)

Sénécal

Bridon

et al. 2014

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“…This model has been proven to be reliable and close to the reality for homogeneous hexagonal networks [14] [15], as well as for heterogeneous networks [6].…”

Section: B Calculation Of the Sinrmentioning

confidence: 99%

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

2014 IEEE Globecom Workshops (GC Wkshps)

Sénécal

Coupechoux

et al. 2014

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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.

“…Authors of [4] [5] analyzed the LTE wireless network heterogeneity and proposed a model evaluating the impact on coverage and throughput, showing that QoS criteria can be satisfied with reduced emitted power by the way of a spatial redeployment of stations or densification. The effect of mobility on α-fair scheduling policies is evaluated in [3].…”

Section: Introductionmentioning

confidence: 99%

Impact of mobility on QoS in heterogeneous wireless networks

Touati

2015 IEEE International Conference on Communication Workshop (ICCW)

Coupechoux

2015

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This paper develops a model evaluating the impact of mobility in heterogeneous wireless networks by the way of analytical expressions of the spatio-temporal evolution of QoS indicators. Such formulas are obtained by introducing a multiuser averaged mobility pattern named density of users. Among the set of densities, we use a Gaussian form of this quantity, which results from a modeling method based on the maximum entropy principle. Numerical results show the temporal variations of the QoS indicators and highlight the combined effects of network heterogeneity due to the presence of macro and small cells, and traffic variations due to mobility.