Table 1.
Vegetation attenuation coefficients [35].
Fig 1.
Attenuation caused by leaves across varying frequencies.
This figure illustrates how dielectric properties of leaves, primarily influenced by moisture, result in frequency-dependent signal losses.
Fig 2.
Electromagnetic loss induced by tree branches versus frequency.
The attenuation trends are derived based on the water-like dielectric behavior of moist wood, emphasizing stronger losses at higher frequencies. The figure shows also branches attenuation according to the ITU model based on measurements [35].
Table 2.
Dielectric Constant and Loss Tangent of Wood (Moisture = 40%, Temp = 20°C) [35].
Fig 3.
Configuration of the two patch antennas oriented at and
with respect to the vertical axis, mounted on the tilted plane.
This setup ensures orthogonal polarization states for the MIMO system, providing the basis for the analysis of polarization-specific losses and phase shift in vegetated channels.
Fig 4.
The orthogonally polarized waves in the tilted plane are first decomposed into vertical and horizontal planes.
The vertical plane components are then further decomposed into vertical and horizontal directions.
Fig 5.
Schematic depiction of UAV-ground communication geometry and relevant spatial parameters.
It illustrates vegetation depth, UAV altitude, user unit height, and elevation angle between UAV and user unit.
Fig 6.
Dielectric constant (real part of ) of vegetation for both polarizations across frequency.
This figure visualizes how vegetated environments influence real permittivity, differentiating between horizontal and vertical wave interactions (A1 = 1.048 and ).
Fig 7.
Loss factor of vegetation () for both polarizations across frequency.
This figure visualizes how vegetated environments influence imaginary permittivity component, differentiating between horizontal and vertical wave interactions (A1 = 1.048 and ).
Table 3.
Summary of key findings from major vegetation propagation studies.
Fig 8.
Spatial distribution of user positions (X,Y) within the designated area covered by the UAV communication link.
Fig 9.
Cross Polarization Discrimination as a function of frequency for the dielectric properties defined by Figs 6 and 7.
A user is selected whose angle of elevation with the UAV is and the UAV height is 200 m.
Fig 10.
Maximum value of XPD of all users is recorded while UAV lateral position varies keeping its height at 200 m.
Fig 11.
Variation of Cross-Polarization Discrimination (XPD) with elevation angle for a fixed UAV height of 200 m.
Elevation angles are varied from 20o to 89o, and the maximum XPD values are computed across different operating frequencies. The figure highlights how XPD behavior changes with elevation angle.
Fig 12.
A user is selected and its XPD value is calculated while UAV lateral position varies keeping its altitude at .
Fig 13.
XPD values for all users without applying the optimization algorithm, with UAV positioned at ,
, and
.
The figure illustrates that, in the absence of optimization, some users experience extremely high XPD values, leading to severe interference between MIMO channels and potential information loss.
Fig 14.
Optimal X-coordinate of the UAV position.
Fig 15.
Optimal Y-coordinate of the UAV position.
Fig 16.
Optimal UAV altitude derived from the optimization algorithm.
Fig 17.
Maximum cross-polarization discrimination (XPD) observed for the worst-case user location within the coverage area.
Fig 18.
XPD values for all users after applying the optimization algorithm to determine the optimal UAV position.
Compared to Fig 13, XPD values are significantly reduced and remain below 0.42 for all users, ensuring minimal inter-channel interference and a satisfactory Quality of Service (QoS) in the MIMO system.
Fig 19.
CSA runtime and convergence profile.
Fig 20.
QPSO runtime and convergence profile.
Fig 21.
Random search runtime and convergence profile.
Table 4.
Comparison of runtime and optimization performance among CSA, QPSO, and Random Search.
Fig 22.
Effective SNR versus XPD.
Fig 23.
BER versus XPD (QPSK).
Fig 24.
Crosstalk from channel to
channel due to propagation in anisotropic medium.
Fig 25.
Crosstalk from channel to
channel due to Propagation in anisotropic medium.