Abstract
In cognitive wireless networks, active monitoring of the wireless environment is often performed through advanced spectrum sensing and network sniffing. This leads to a set of spatially distributed measurements which are collected from different sensing devices. Nowadays, several interpolation methods (e.g., Kriging) are available and can be used to combine these measurements into a single globally accurate radio environment map that covers a certain geographical area. However, the calibration of multi-fidelity measurements from heterogeneous sensing devices, and the integration into a map is a challenging problem. In this paper, the auto-regressive co-Kriging model is proposed as a novel solution. The algorithm is applied to model measurements which are collected in a heterogeneous wireless testbed environment, and the effectiveness of the new methodology is validated.
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Acknowledgments
The research activities that have been described in this paper were funded by Ghent University, iMinds, the Fund for Scientific Research in Flanders (FWO-V) Project G.0325.11N and the Interuniversity Attraction Poles Programme BESTCOM initiated by the Belgian Science Policy Office. This paper is also the result of research carried out as part of the QoCON project funded by iMinds. QoCON is being carried out by a consortium of the industrial partners: Televic, Option and Barco in cooperation with iMinds research groups: IBCN (UGent), WiCa (UGent), SMIT (VUB), PATS (UA) and IMEC. D. Deschrijver and I. Couckuyt are post-doctoral research fellows of FWO-V.
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Ulaganathan, S., Deschrijver, D., Pakparvar, M. et al. Building accurate radio environment maps from multi-fidelity spectrum sensing data. Wireless Netw 22, 2551–2562 (2016). https://doi.org/10.1007/s11276-015-1111-0
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DOI: https://doi.org/10.1007/s11276-015-1111-0