Advanced search
2 files | 1.12 MB Add to list
Author
Organization
Abstract
Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.
Keywords
Coverage, Estimation, Machine Learning, Received Signal, REM

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 714.94 KB
  • WICA 1100a.pdf
    • full text (Accepted manuscript)
    • |
    • open access
    • |
    • PDF
    • |
    • 407.15 KB

Citation

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

MLA
Hervis Santana, Yosvany, et al. “Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels.” 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), IEEE, 2022, doi:10.1109/bmsb55706.2022.9828582.
APA
Hervis Santana, Y., Plets, D., Martinez Alonso, R., Nieto, G. G., Martens, L., & Joseph, W. (2022). Radio environment map of an LTE deployment based on machine learning estimation of signal levels. 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). Presented at the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB 2022), Bilbao, Spain. https://doi.org/10.1109/bmsb55706.2022.9828582
Chicago author-date
Hervis Santana, Yosvany, David Plets, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, and Wout Joseph. 2022. “Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels.” In 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE. https://doi.org/10.1109/bmsb55706.2022.9828582.
Chicago author-date (all authors)
Hervis Santana, Yosvany, David Plets, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, and Wout Joseph. 2022. “Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels.” In 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE. doi:10.1109/bmsb55706.2022.9828582.
Vancouver
1.
Hervis Santana Y, Plets D, Martinez Alonso R, Nieto GG, Martens L, Joseph W. Radio environment map of an LTE deployment based on machine learning estimation of signal levels. In: 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). IEEE; 2022.
IEEE
[1]
Y. Hervis Santana, D. Plets, R. Martinez Alonso, G. G. Nieto, L. Martens, and W. Joseph, “Radio environment map of an LTE deployment based on machine learning estimation of signal levels,” in 2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Bilbao, Spain, 2022.
@inproceedings{01GX36XYPXKB35WX7JAGRXKTSZ,
  abstract     = {{Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.}},
  author       = {{Hervis Santana, Yosvany and Plets, David and Martinez Alonso, Rodney and Nieto, Glauco Guillen and Martens, Luc and Joseph, Wout}},
  booktitle    = {{2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)}},
  isbn         = {{9781665469012}},
  issn         = {{2155-5044}},
  keywords     = {{Coverage,Estimation,Machine Learning,Received Signal,REM}},
  language     = {{eng}},
  location     = {{Bilbao, Spain}},
  pages        = {{6}},
  publisher    = {{IEEE}},
  title        = {{Radio environment map of an LTE deployment based on machine learning estimation of signal levels}},
  url          = {{http://doi.org/10.1109/bmsb55706.2022.9828582}},
  year         = {{2022}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: