Regression analysis‐based identification model of key impactful parameters on buried antennas. (27th November 2021)

• Record Type: Journal Article
• Authors: El Mejjatti, Mounir; Habbani, Ahmed; El Bouanani, Faissal
• Is Part Of: International journal of communication systems. Volume 35:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-27
• Subjects: antennas -- internet of underground things -- multiple linear regression analysis -- wireless communication -- wireless underground sensors network; Telecommunication systems -- Periodicals; 621.382
• DOI: 10.1002/dac.5051
• Languages: English
• ISSNs: 1074-5351

Abstract

Summary: 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. Abstract : In this article, three new multivariable regression models for predicting buried antennas' resonance frequency are elaborated. Specifically, the models are based on three critical soil parameters, namely, volumetric moisture, bulk density, and temperature.