Spectrum sensing in cognitive radio networks: the cooperation-processing tradeoff
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
An Overview of Scaling Laws in Ad Hoc and Cognitive Radio Networks
Wireless Personal Communications: An International Journal
Assessment of urban-scale wireless networks with a small number of measurements
Proceedings of the 14th ACM international conference on Mobile computing and networking
Spatial statistics and models of spectrum use
Computer Communications
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
Estimation of maximum interference-free power level for opportunistic spectrum access
IEEE Transactions on Wireless Communications
IEEE 802.22: the first cognitive radio wireless regional area network standard
IEEE Communications Magazine
IEEE Transactions on Mobile Computing
A survey of spectrum sensing algorithms for cognitive radio applications
IEEE Communications Surveys & Tutorials
Cognitive radio in a frequency-planned environment: some basic limits
IEEE Transactions on Wireless Communications - Part 1
Multicarrier communication techniques for spectrum sensing and communication in cognitive radios
IEEE Communications Magazine
Full length article: Positioning-based framework for secondary spectrum usage
Physical Communication
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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We study the performance characteristics of cognitive wireless networks under different dynamic spectrum access scenarios. Our focus is especially on the influence of spatial structures of the primary and secondary user networks on the achievable performance of the secondary network. We adopt techniques from spatial statistics to develop stochastic models for the structure and interaction of these networks. The chosen models are based on Gaussian random fields and Gibbs point processes, and are firmly grounded on empirical data. We then apply extensive Monte Carlo simulations to study the behavior of these models and their related performance properties. The models and the applied techniques are applicable also to a wider variety of networking problems. Our results provide first quantitative assessments on the influence of the spatial structure, and especially correlation properties, of the involved networks on the expected performance and thus on the utility of dynamic spectrum access based systems.