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White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints

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Abstract
Spectrum surveys performed worldwide demonstrate that the spectrum utilization efficiency is less than 0.25. Therefore, the traditional long-term licensed spectrum allocation by regulators is not sustainable. Although dynamic spectrum access networks allow increasing the efficiency of spectrum utilization, coexistence is still a major problem. In this paper, we investigate the capability of machine learning for estimating the white space availability based on a dataset from a spectrum survey from 170 MHz to 1 GHz. In addition, we present an algorithm for minimizing the effect of hidden nodes on wrongful spectrum allocation and interference. Our optimization algorithm based on supervised machine learning allows increasing the spectrum utilization efficiency with a factor 5 (from 0.09 to 0.47) in the surveyed region. In addition, our algorithm allows decreasing the interference probability caused by the effect of hidden nodes by a factor 6, compared to the traditional distributed allocation of spectrum in Dynamic Spectrum Access networks.
Keywords
Dynamic spectrum access, Cognitive radio, Spectrum survey, Spectrum efficiency, White spaces, Hidden nodes, Machine learning, ALGORITHM, NETWORKS

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Citation

Please use this url to cite or link to this publication:

MLA
Martinez Alonso, Rodney, et al. “White Spaces Pattern Finding and Inference Based on Machine Learning for Multi-Frequency Spectrum Footprints.” COMPUTER NETWORKS, vol. 233, 2023, doi:10.1016/j.comnet.2023.109871.
APA
Martinez Alonso, R., Plets, D., Martens, L., Joseph, W., Pupo, E. F., & Nieto, G. G. (2023). White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints. COMPUTER NETWORKS, 233. https://doi.org/10.1016/j.comnet.2023.109871
Chicago author-date
Martinez Alonso, Rodney, David Plets, Luc Martens, Wout Joseph, Ernesto Fontes Pupo, and Glauco Guillen Nieto. 2023. “White Spaces Pattern Finding and Inference Based on Machine Learning for Multi-Frequency Spectrum Footprints.” COMPUTER NETWORKS 233. https://doi.org/10.1016/j.comnet.2023.109871.
Chicago author-date (all authors)
Martinez Alonso, Rodney, David Plets, Luc Martens, Wout Joseph, Ernesto Fontes Pupo, and Glauco Guillen Nieto. 2023. “White Spaces Pattern Finding and Inference Based on Machine Learning for Multi-Frequency Spectrum Footprints.” COMPUTER NETWORKS 233. doi:10.1016/j.comnet.2023.109871.
Vancouver
1.
Martinez Alonso R, Plets D, Martens L, Joseph W, Pupo EF, Nieto GG. White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints. COMPUTER NETWORKS. 2023;233.
IEEE
[1]
R. Martinez Alonso, D. Plets, L. Martens, W. Joseph, E. F. Pupo, and G. G. Nieto, “White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints,” COMPUTER NETWORKS, vol. 233, 2023.
@article{01HGAPQWFZNJYT40GNBAC025PS,
  abstract     = {{Spectrum surveys performed worldwide demonstrate that the spectrum utilization efficiency is less than 0.25. Therefore, the traditional long-term licensed spectrum allocation by regulators is not sustainable. Although dynamic spectrum access networks allow increasing the efficiency of spectrum utilization, coexistence is still a major problem. In this paper, we investigate the capability of machine learning for estimating the white space availability based on a dataset from a spectrum survey from 170 MHz to 1 GHz. In addition, we present an algorithm for minimizing the effect of hidden nodes on wrongful spectrum allocation and interference. Our optimization algorithm based on supervised machine learning allows increasing the spectrum utilization efficiency with a factor 5 (from 0.09 to 0.47) in the surveyed region. In addition, our algorithm allows decreasing the interference probability caused by the effect of hidden nodes by a factor 6, compared to the traditional distributed allocation of spectrum in Dynamic Spectrum Access networks.}},
  articleno    = {{109871}},
  author       = {{Martinez Alonso, Rodney and Plets, David and Martens, Luc and Joseph, Wout and Pupo, Ernesto Fontes and Nieto, Glauco Guillen}},
  issn         = {{1389-1286}},
  journal      = {{COMPUTER NETWORKS}},
  keywords     = {{Dynamic spectrum access,Cognitive radio,Spectrum survey,Spectrum efficiency,White spaces,Hidden nodes,Machine learning,ALGORITHM,NETWORKS}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{White spaces pattern finding and inference based on machine learning for multi-frequency spectrum footprints}},
  url          = {{http://doi.org/10.1016/j.comnet.2023.109871}},
  volume       = {{233}},
  year         = {{2023}},
}

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