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We propose and analyze a new shadowing field model meant to capture spatialcorrelations. The interference field associated with this new model is comparedto that of the widely used independent shadowing model. Independent shadowingover links is adopted because of the resulting closed forms for performancemetrics, and in spite of the well-known fact that the shadowing fields ofnetworks are spatially correlated. The main purpose of this paper is tochallenge this independent shadowing approximation. For this, we analyze theinterference measured at the origin in networks where 1) nodes which are in thesame cell of some random shadowing tessellation share the same shadow, or 2)nodes which share a common mother point in some cluster process share the sameshadow. By leveraging stochastic comparison techniques, we give the orderrelation of the three main user performance metrics, namely coverageprobability, Shannon throughput and local delay, under both the correlated andthe independent shadowing assumptions. We show that the evaluation of theconsidered metrics under the independent approximation is systematicallypessimistic compared to the correlated shadowing model. The improvement in eachmetric when adopting the correlated shadow model is quantified and shown to bequite significant.

On the Effect of Shadowing Correlation on Wireless Network Performance