A spatial statistics approach to characterizing and modeling the structure of cognitive wireless networks

The performance of cognitive wireless networks (CWNs) depends heavily on their spatial structure. However, highly simplified models are still routinely used for performance evaluation of CWNs and other wireless networks, with node locations often being assumed to be uniformly and randomly distributed in a given region. In this paper we apply techniques from spatial statistics literature to show that this assumption is not valid for a wide variety of existing networks, and neither can it be expected to hold for future cognitive wireless networks. We also develop improved models of the spatial structure of the network for a variety of wireless network types. In particular, we construct models of television and radio transmitter distributions as well as different types of cellular and Wi–Fi networks that have direct applications in cognitive wireless networks research. We use a stochastic approach based on fitting parametric location models to empirical data. Our results strongly indicate that the so-called Geyer saturation model can accurately reproduce the spatial structure of a large variety of wireless network types, arising from both planned or chaotic deployments. The resulting models can be used in simulations or as basis of analytical calculations to study different network properties. They can be also used within CWNs for on-line reasoning about the surrounding radio environment.