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While modeling and analysis of network topology has been an active area of research in fixed networks, much less work has been done towards realistic modeling of wireless networks. The graph- based approach that has served as solid foundation for network science in the fixed domain is not natural for wireless communication networks, since their performance inherently depends on the spatial relationships between nodes. In this paper we apply techniques from spatial statistics literature to develop 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 that have applications in, for example, cognitive wireless network applications. We use a stochastic approach based on fitting parametric location models to empirical data. Our results 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 of different network properties, and we believe that the presented methodology can serve as a solid foundation for the emerging network science of wireless communication networks.