Downlink Fluid Model of CDMA Networks

2009 IEEE Wireless Communications and Networking Conference

2009

Abstract-In this paper, we develop an analytical study of the mobility in cellular networks and its impact on quality of service and outage probability. We first express analytically the downlink other-cell interference factor f by using a fluid model network. It is defined here as the ratio of outer cell received power to the inner cell received power. It allows us to analyze users mobility and to derive expressions of the outage probability. We show that mobility can modify the capacity of a cell and we quantify the outage probability variations. We moreover establish how mobility plays a role in quality of service management. All results are compared to Monte Carlo simulations performed in a traditional hexagonal network.

Section: Introductionmentioning

confidence: 77%

On the Impact of Mobility on Outage Probability in Cellular Networks

2009 IEEE Wireless Communications and Networking Conference

2009

“…The fluid network model consists in replacing a given fixed finite number of transmitters by an equivalent continuous density of transmitters [14] [15]. Given an inter site distance 2R c , interferers are characterized by a density ρ BS of BS starting at a distance of 2R c from a BS (covering a zone of radius R c ), as illustrated on Fig.…”

Section: A Fluid Networkmentioning

confidence: 99%

“…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

Sénécal

2014 IEEE Globecom Workshops (GC Wkshps)

et al. 2014

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.

“…As a consequence, we need to compute the spatial mean and standard deviation of f 0 . For a mobile at the distance r from its serving base station, and dropping the dependance with η, the interference factor can be written as f 0 (r) [8] [9]:…”

Section: A Outage Probability Without Shadowingmentioning

confidence: 99%

Impact of Topology and Shadowing on the Outage Probability of Cellular Networks

2009 IEEE International Conference on Communications

2009

Abstract-This paper proposes an analytical study of the shadowing impact on the outage probability in cellular radio networks. We establish that the downlink other-cell interference factor, f , which is defined here as the ratio of outer cell received power to the inner cell received power, plays a fundamental role in the outage probability. From f , we are able to derive the outage probability of a mobile station (MS) initiating a new call. Taking into account the shadowing, f is expressed as a lognormal random variable. Analytical expressions of the interference factor's mean m f and standard deviation s f are provided in this paper. These expressions depend on the topology of the network characterized by a G factor. We show that shadowing increases the outage probability, and using our analytical method, we are able to quantify this impact. However, we establish that the network topology, or correlated received powers, may limit this increase.