Impact of interferences on connectivity in ad hoc networks
IEEE/ACM Transactions on Networking (TON)
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
In search of the elusive ground truth: the internet's as-level connectivity structure
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Assessment of urban-scale wireless networks with a small number of measurements
Proceedings of the 14th ACM international conference on Mobile computing and networking
Stochastic geometry and random graphs for the analysis and design of wireless networks
IEEE Journal on Selected Areas in Communications - Special issue on stochastic geometry and random graphs for the analysis and designof wireless networks
Interference and outage in clustered wireless ad hoc networks
IEEE Transactions on Information Theory
IEEE 802.22: the first cognitive radio wireless regional area network standard
IEEE Communications Magazine
Statistical characterization of transmitter locations based on signal strength measurements
ISWPC'10 Proceedings of the 5th IEEE international conference on Wireless pervasive computing
The capacity of wireless networks
IEEE Transactions on Information Theory
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
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.