VHF–UHF Electric Field Prediction for Amazon City Using Dyadic Green’s Function

Silva, Diego K. N. da; Eras, Leslye Estefania Castro; Barrsos, Fabricio J. B.; Cavalcante, Gervásio P. S.

doi:10.1007/s11277-020-07325-y

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…In certain situations, unidirectional propagation modelling may represent communication channels in both ways owing to the reciprocity principle [ 11 ]. However, in this study, the propagation path between the transmitter and receiver contained a blend of open areas, buildings, and densely forested areas, that is, the terrain was not isotropic [ 18 ], and the reciprocity principle could not be used. Hence, it is relevant to conduct a comprehensive RPC study considering LOS and NLOS for UAV-enabled wireless networks in the most diverse cases.…”

Section: Related Workmentioning

confidence: 99%

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

Cardoso

Barros

Carvalho

et al. 2022

Sensors

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The 5G deployment brings forth the usage of Unmanned Aerial Vehicles (UAV) to assist wireless networks by providing improved signal coverage, acting as relays or base-stations. Another technology that could help achieve 5G massive machine-type communications (mMtc) goals is the Long Range Wide-Area Network (LoRaWAN) communication protocol. This paper studied these complementary technologies, LoRa and UAV, through measurement campaigns in suburban, densely forested environments. Downlink and uplink communication at different heights and spreading factors (SF) demonstrate distinct behavior through our analysis. Moreover, a neural network was trained to predict the measured signal-to-noise ratio (SNR) behavior and results compared with SNR regression models. For the downlink scenario, the neural network results show a root mean square error (RMSE) variation between 1.2322–1.6623 dB, with an error standard deviation (SD) less than 1.6730 dB. For the uplink, the RMSE variation was between 0.8714–1.3891 dB, with an error SD less than 1.1706 dB.

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Section: Related Workmentioning

confidence: 99%

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

Cardoso

Barros

Carvalho

et al. 2022

Sensors

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“…Tamir [23] has established a three-layer forest model to study radio wave propagation in forests. Recently, DGF in two and three-layered medium model was proposed to obtain a propagation model of the typical Amazon city [24]. This paper is intended to develop a new method for solving the electromagnetic scattering problem in two-layer media.…”

Section: Introductionmentioning

confidence: 99%

Dyadic Green’s Function and the Application of Two-Layer Model

Gu¹,

Shi²,

Zhang³

et al. 2020

Mathematics

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Dyadic Green’s function (DGF) is a powerful and elegant way of solving electromagnetic problems in the multilayered media. In this paper, we introduce the electric and magnetic DGFs in free space, respectively. Furthermore, the symmetry of different kinds of DGFs is proved. This paper focuses on the application of DGF in a two-layer model. By introducing the universal form of the vector wave functions in space rectangular, cylindrical and spherical coordinates, the corresponding DGFs are obtained. We derive the concise and explicit formulas for the electric fields represented by a vertical electric dipole source. It is expected that the proposed DGF can be extended to some more electric field problem in the two-layer model.

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VHF–UHF Electric Field Prediction for Amazon City Using Dyadic Green’s Function

Cited by 2 publications

References 18 publications

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

SNR Prediction with ANN for UAV Applications in IoT Networks Based on Measurements

Dyadic Green’s Function and the Application of Two-Layer Model

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