NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Modeling spatially correlated data in sensor networks
ACM Transactions on Sensor Networks (TOSN)
Experimental study of spectrum sensing based on energy detection and network cooperation
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Spatial statistics of spectrum usage: from measurements to spectrum models
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Spectrum sensing measurements of pilot, energy, and collaborative detection
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
An overview of spectrum occupancy models for cognitive radio networks
NETWORKING'11 Proceedings of the IFIP TC 6th international conference on Networking
Spatio-temporal spectrum modeling: Taxonomy and economic evaluation of context acquisition
Telecommunications Policy
Influence of spatial statistics of spectrum use on the performance of cognitive wireless networks
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
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In order to opportunistically exploit unused radio spectrum nodes of dynamic spectrum access (DSA) networks monitor the spectrum around them. Such cognitive radios can greatly benefit from a spatial characterization of spectrum use. However, there is need to find an efficient way to describe spatial use, something which has not been studied in details so far. In this paper, we introduce spatial statistics techniques as promising methods to describe spectrum use and enable optimization of DSA networks. We discuss two approaches to spatial modelling of spectrum, namely a deterministic approach based on a system model of the complete radio environment and an empirical approach that exploits passive measurements of the spectrum use. We elaborate on the impact of different network properties on the models and provide realistic parameter sets for generation of simulation scenarios. Additionally, we investigate cooperative sensing as a use case for spatial statistics based runtime optimization of the network configuration.