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Over the past decade, many works on the modeling of wireless networks usingstochastic geometry have been proposed. Results about probability of coverage,throughput or mean interference, have been provided for a wide variety ofnetworks (cellular, ad-hoc, cognitive, sensors, etc). These results notablyallow to tune network protocol parameters. Nevertheless, in their vastmajority, these works assume that the wireless network deployment is flat:nodes are placed on the Euclidean plane. However, this assumption is disprovedin dense urban environments where many nodes are deployed in high buildings. Inthis letter, we derive the exact form of the probability of coverage for thecases where the interferers form a 3D Poisson Point Process (PPP) and a 3DModified Matern Process (MMP), and compare the results with the 2D case. Themain goal of this letter is to show that the 2D model, although being the mostcommon, can lead either to an optimistic or a pessimistic evaluation of theprobability of coverage depending on the parameters of the model.

Modeling Dense Urban Wireless Networks with 3D Stochastic Geometry