Parameter space exploration with Gaussian process trees
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Software-defined radio: basics and evolution to cognitive radio
EURASIP Journal on Wireless Communications and Networking
Bayesian treed gaussian process models
Bayesian treed gaussian process models
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
WiFi-SLAM using Gaussian process latent variable models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Utilization of Location Information in Cognitive Wireless Networks
IEEE Wireless Communications
IEEE Journal on Selected Areas in Communications
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The communication space is five dimensional: its degrees of freedom are frequency, time and space. The use of the electromagnetic spectrum depends on these parameters. With future applications such as opportunistic overlay access or distributed spectrum monitoring in mind, it is important to estimate the state of the communication space on the basis of incomplete or imprecise information. A promising approach are technology centric Cognitive Radio networks. In these networks, nodes cooperate to infer information on spectral occupancy. This conceptual paper proposes a novel approach for centralized modeling of the communication space with emphasis on spatial dependencies through the use of a regression model. The modeling approach is verified with practical measurements.