Regression analysis-based identification model of key impactful parameters on buried antennas

TitreRegression analysis-based identification model of key impactful parameters on buried antennas
Publication TypeJournal Article
Year of Publication2022
AuthorsM. Mejjatti, E, Habbani, A, F. Bouanani, E
JournalInternational Journal of Communication Systems
Mots-clés%moisture, Antenna bandwidth, Antennas, Bulk density, Identification modeling, Internet of underground thing, Linear regression, Moisture, Moisture determination, Multiple linear regression analyses (MLRA), Natural frequencies, Permittivity, Propagation media, Volumetrics, Wireless communications, Wireless underground sensor networks

The antenna's bandwidth is strongly dependent on its physical properties and the environment where it is located. Nevertheless, for transmitting devices embedded permanently in the soil, the propagation medium's properties are unstable due to dielectric permittivity's spatial and temporal changes. Relying on soil parameters values, we present three new multivariable regression models allowing a good fit to the dataset curves with antenna's resonant frequency variations prediction of about 98%. Precisely, such models have been obtained with the help of the multiple linear regression analysis method. Additionally, the results revealed that amongst four considered parameters (i.e., volumetric moisture, bulk density, specific density, and temperature), volumetric moisture is the most influencing factor on the antenna operating frequency band with a correlation coefficient of −0.989, whereas it equals 0.04, −0.035, and −0.002 for the temperature, bulk density, and specific density, respectively. © 2021 John Wiley & Sons Ltd.




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