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Building accurate radio environment maps from multi-fidelity spectrum sensing data

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)
(2016) WIRELESS NETWORKS. 22(8). p.2551-2562
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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

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Chicago
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.
APA
Ulaganathan, S., Deschrijver, D., Pakparvar, M., Couckuyt, I., Liu, W., Plets, D., Joseph, W., et al. (2016). Building accurate radio environment maps from multi-fidelity spectrum sensing data. WIRELESS NETWORKS, 22(8), 2551–2562.
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.
MLA
Ulaganathan, Selvakumar et al. “Building Accurate Radio Environment Maps from Multi-fidelity Spectrum Sensing Data.” WIRELESS NETWORKS 22.8 (2016): 2551–2562. Print.
@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://dx.doi.org/10.1007/s11276-015-1111-0},
  volume       = {22},
  year         = {2016},
}

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