On the Effect of Shadowing Correlation on Wireless Network Performance

TL;DR

This paper introduces a new spatially correlated shadowing model for wireless networks, compares it to the independent shadowing model, and demonstrates that the independent assumption is pessimistic, with correlated models showing significant performance improvements.

Contribution

The paper develops a novel shadowing correlation model and analyzes its impact on network performance metrics, challenging the common independent shadowing approximation.

Findings

Independent shadowing underestimates performance metrics.

Correlated shadowing significantly improves coverage probability.

Performance metrics are systematically better with correlated shadowing.

Abstract

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.