The Cognitive radio is a strong and promising paradigm for the next generation self-adaptive smart radios and networks. In this paper we study the potential applications of the topology information in a cognitive radio environment. We argue that use of network topology information, including here the geometric relations of the nodes, can bring significant benefits to cognitive radios and networks. We believe that in the case of wireless networks, and especially in the case of cooperative cognitive radios, it is extremely valuable to know more about the network topology than just neighbourhood counts. The topology information has direct usage and implications on connectivity and capacity estimates of the network, and is a key quantity to consider in making of network optimization decisions. As a "proof of concept" for out ideas, we present a rather detailed and comprehensive topology information characterization study using spatial statistic models. We introduce particularly N-point spatial correlation functions and demonstrate their usability. Furthermore we report new results on correlated locations on WiFi access point locations as an example.
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