  We propose and analyze a new shadowing field model meant to capture spatial
correlations. The interference field associated with this new model is compared
to that of the widely used independent shadowing model. Independent shadowing
over links is adopted because of the resulting closed forms for performance
metrics, and in spite of the well-known fact that the shadowing fields of
networks are spatially correlated. The main purpose of this paper is to
challenge this independent shadowing approximation. For this, we analyze the
interference measured at the origin in networks where 1) nodes which are in the
same 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 same
shadow. By leveraging stochastic comparison techniques, we give the order
relation of the three main user performance metrics, namely coverage
probability, Shannon throughput and local delay, under both the correlated and
the independent shadowing assumptions. We show that the evaluation of the
considered metrics under the independent approximation is systematically
pessimistic compared to the correlated shadowing model. The improvement in each
metric when adopting the correlated shadow model is quantified and shown to be
quite significant.
