Abstract
With the current focus on 5G and 6G wireless systems and the move from sub-6 GHz frequencies band to millimetre-wave band, likely, the density of base stations will also increase. The increase in density may result in deployment in non-optimum locations where there may be significant penetration through the foliage. Models for the scattering from vegetation components (i.e. leaves, twigs and branches) are a primary input into the current ITU-R recommendation P.833-9 for attenuation in vegetation. This thesis reviews the current modelling approaches developed in vegetation scattering models and addresses the relevance and assumptions. Also, the limitation in the model's assumptions recognised and numerical method has been used to relax these assumptions. A full-wave model for leaves, branches and trees have been presented using HFSS for the EM scattering calculations. The resulting scattering cross-section has been determined over azimuthal and elevation angles.
The variability of scattering from vegetation's components due to changes in several parameters, including size, shape, curvature, inhomogeneity in dielectric permittivity and moisture content have been examined. Moreover, the effect of water drops on a leaf surface is modelled, and results are presented. The modelling assumption made in the literature, backed up by measurements, are reasonable in sub-6 GHz bands and up to 10 GHz. However, at millimetre-wave frequencies bands, it is shown that these assumptions begin to break down.
Most current modelling approaches e.g., ITU (2016) consider only a single leaf
classification and number density there is clearly scope for the addition of further
parameters to account for the variations seen in this thesis. It seems unlikely that a single size class and number density is sufficient to formulate a wide frequency model for trees. Additional further parameters are likely to be in the form of distributions in an analogous way to which rain drop-size distributions are used in calculating attenuation due to rain. Further work will consider how these might used to augment the existing approaches.
| Date of Award | 25 May 2022 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Robert Watson (Supervisor) & Ali Mohammadi (Supervisor) |