Building accurate radio environment maps from multi-fidelity spectrum sensing data
- Author
- Selvakumar Ulaganathan (UGent) , Dirk Deschrijver (UGent) , Mostafa Pakparvar (UGent) , Ivo Couckuyt (UGent) , Wei Liu (UGent) , David Plets (UGent) , Wout Joseph (UGent) , Tom Dhaene (UGent) , Luc Martens (UGent) and Ingrid Moerman (UGent)
- Organization
- 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.
- Keywords
- COGNITIVE RADIO, Multi-fidelity modeling, ENGINEERING DESIGN, OPTIMIZATION, METAMODELS, NETWORKS, Radio environment maps, Wireless experimentation, Kriging, IBCN
Downloads
-
6746 i.pdf
- full text
- |
- open access
- |
- |
- 657.86 KB
-
(...).pdf
- full text
- |
- UGent only
- |
- |
- 1.51 MB
Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8165831
- MLA
- Ulaganathan, Selvakumar, et al. “Building Accurate Radio Environment Maps from Multi-Fidelity Spectrum Sensing Data.” WIRELESS NETWORKS, vol. 22, no. 8, 2016, pp. 2551–62, doi:10.1007/s11276-015-1111-0.
- APA
- Ulaganathan, S., Deschrijver, D., Pakparvar, M., Couckuyt, I., Liu, W., Plets, D., … Moerman, I. (2016). Building accurate radio environment maps from multi-fidelity spectrum sensing data. WIRELESS NETWORKS, 22(8), 2551–2562. https://doi.org/10.1007/s11276-015-1111-0
- Chicago author-date
- Ulaganathan, Selvakumar, Dirk Deschrijver, Mostafa Pakparvar, Ivo Couckuyt, Wei Liu, David Plets, Wout Joseph, Tom Dhaene, Luc Martens, and Ingrid Moerman. 2016. “Building Accurate Radio Environment Maps from Multi-Fidelity Spectrum Sensing Data.” WIRELESS NETWORKS 22 (8): 2551–62. https://doi.org/10.1007/s11276-015-1111-0.
- Chicago author-date (all authors)
- Ulaganathan, Selvakumar, Dirk Deschrijver, Mostafa Pakparvar, Ivo Couckuyt, Wei Liu, David Plets, Wout Joseph, Tom Dhaene, Luc Martens, and Ingrid Moerman. 2016. “Building Accurate Radio Environment Maps from Multi-Fidelity Spectrum Sensing Data.” WIRELESS NETWORKS 22 (8): 2551–2562. doi:10.1007/s11276-015-1111-0.
- Vancouver
- 1.Ulaganathan S, Deschrijver D, Pakparvar M, Couckuyt I, Liu W, Plets D, et al. Building accurate radio environment maps from multi-fidelity spectrum sensing data. WIRELESS NETWORKS. 2016;22(8):2551–62.
- IEEE
- [1]S. Ulaganathan et al., “Building accurate radio environment maps from multi-fidelity spectrum sensing data,” WIRELESS NETWORKS, vol. 22, no. 8, pp. 2551–2562, 2016.
@article{8165831,
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.}},
author = {{Ulaganathan, Selvakumar and Deschrijver, Dirk and Pakparvar, Mostafa and Couckuyt, Ivo and Liu, Wei and Plets, David and Joseph, Wout and Dhaene, Tom and Martens, Luc and Moerman, Ingrid}},
issn = {{1022-0038}},
journal = {{WIRELESS NETWORKS}},
keywords = {{COGNITIVE RADIO,Multi-fidelity modeling,ENGINEERING DESIGN,OPTIMIZATION,METAMODELS,NETWORKS,Radio environment maps,Wireless experimentation,Kriging,IBCN}},
language = {{eng}},
number = {{8}},
pages = {{2551--2562}},
title = {{Building accurate radio environment maps from multi-fidelity spectrum sensing data}},
url = {{http://doi.org/10.1007/s11276-015-1111-0}},
volume = {{22}},
year = {{2016}},
}
- Altmetric
- View in Altmetric
- Web of Science
- Times cited: