Effect of non-uniform traffic distributions on performance of a cellular CDMA system

Ganesh, R.; Joseph, K.

doi:10.1109/icupc.1997.627234

Cited by 18 publications

(7 citation statements)

References 6 publications

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“…Naturally, such flexibility is desirable, especially to study the current capacity-centric deployments where BSs are specifically deployed to be proximate to areas of high UE density. The most popular and perhaps the only available option to handle such scenarios is through detailed system level simulations, which are both time consuming and have to be focused on a limited range of system parameters [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Modeling Non-Uniform UE Distributions in Downlink Cellular Networks

Dhillon¹,

Ganti

Andrews³

2013

IEEE Wireless Commun. Lett.

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A recent way to model and analyze downlink cellular networks is by using random spatial models. Assuming user equipment (UE) distribution to be uniform, the analysis is performed at a typical UE located at the origin. While this method of sampling UEs provides statistics averaged over the UE locations, it is not possible to sample cell interior and cell edge UEs separately. This complicates the problem of analyzing deployment scenarios involving non-uniform distribution of UEs, especially when the locations of the UEs and the base stations (BSs) are dependent. To facilitate this separation, we propose a new tractable method of sampling UEs by conditionally thinning the BS point process and show that the resulting framework can be used as a tractable generative model to study cellular networks with non-uniform UE distribution.Comment: Submitted to IEEE Wireless Communications Letter

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Section: Introductionmentioning

confidence: 99%

Modeling Non-Uniform UE Distributions in Downlink Cellular Networks

Dhillon¹,

Ganti

Andrews³

2013

IEEE Wireless Commun. Lett.

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“…Sometimes, Probability Density Functions (PDFs) for users' position traffic are conjectured based on assumptions [6] [7]. Though, the most common hypothesis of nodes distribution used among researchers is founded on the uniform density [8]- [16].…”

Section: Introductionmentioning

confidence: 99%

“…Now, we substitute (10) into (9); then the result is used in the expression of (7). Further, to simplify the integrand, we .…”

mentioning

confidence: 99%

Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes

Abdulla

Shayan

2010

2010 IEEE International Conference on Communications

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The emulation of wireless nodes spatial position is a practice used by deployment engineers and network planners to analyze the characteristics of a network. In particular, nodes geolocation will directly impact factors such as connectivity, signals fidelity, and service quality. In literature, in addition to typical homogenous scattering, normal distribution is frequently used to model mobiles concentration in a cellular system. Moreover, Gaussian dropping is often considered as an effective placement method for airborne sensor deployment. Despite the practicality of this model, getting the network channel loss distribution still relies on exhaustive Monte Carlo simulation. In this paper, we argue the need for this inefficient approach and hence derived a generic and exact closed-form expression for the path-loss distribution density between a base-station and a network of nodes. Simulation was used to reaffirm the validity of the theoretical analysis using values from the new IEEE 802.20 standard.Comment: Proc. of IEEE International Conference on Communications (ICC'10

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“…Further, in addition to looking at different cell contours, multiple researchers were also interested by the effect that clustering and user distributions may have on a network [4][5][6][7][8][9]. In short, they explored various nodes dropping near the BS, the edges and in an annulus within a cell.…”

Section: Introductionmentioning

confidence: 99%

Cellular-based Statistical Model for Mobile Dispersion

Abdulla

Shayan

2009

2009 IEEE 14th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks

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Abstract-While analyzing mobile systems we often approximate the actual coverage surface and assume an ideal cell shape. In a multi-cellular network, because of its tessellating nature, a hexagon is more preferred than a circular geometry. Despite this reality, perhaps due to the inherent simplicity, only a model for circular based random spreading is available. However, if used, this results an unfair terminal distribution for non-circular contours. Therefore, in this paper we specifically derived an unbiased node density model for a hexagon. We then extended the principle and established stochastic ways to handle sectored cells. Next, based on these mathematical findings, we created a generic modeling tool that can support a complex network with varying position, capacity, size, user density, and sectoring capability. Last, simulation was used to verify the theoretical analysis.

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Effect of non-uniform traffic distributions on performance of a cellular CDMA system

Cited by 18 publications

References 6 publications

Modeling Non-Uniform UE Distributions in Downlink Cellular Networks

Modeling Non-Uniform UE Distributions in Downlink Cellular Networks

Closed-Form Path-Loss Predictor for Gaussianly Distributed Nodes

Cellular-based Statistical Model for Mobile Dispersion

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