# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 5 Experimental Data Validation
* Tree canopies are modeled simply as cylinders with radius of 1.8 m and height of 8 m with the bottom cap at the height of 1.5 m, i.e., the same as the Rx height. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scatterer** & **Radius (cm)** & **Length/Thickness (cm)** & **Number Density (m\({}^{-3}\))** \\ \hline Branch category 1 & 11.4 & 131 & 0.2 \\ Branch category 2 & 6.0 & 99 & 0.2 \\ Branch category 3 & 2.8 & 82 & 2 \\ Branch category 4 & 0.7 & 54 & 20 \\ Branch category 5 & 0.2 & 12 & 100 \\ Leaf & 3.7 & 0.02 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Physical parameters of branches and leaves utilized in the simulation. Figure 9: The selected measurement scenario represented by cylindrical tree canopies: (**a**) Tx positions (black line) and tree canopies (green cylinders); (**b**) detail of the alley with Rx marked by the red asterisk, tree canopies represented by the green cylinders and Tx-Rx directions shown by the black lines. The Cartesian coordinate system is aligned so that the origin is at the Rx position, the \(x\), \(y\), and \(z\) axes point towards the east, north and zenith, respectively. Tx moving direction is denoted by the arrow. * Ground reflections are not included. * The simulation is carried out for a vertically polarized incident field, although the experimental data were obtained for the circularly polarized field. * Tree trunks were not considered. Now we may analyze the total field time series generated by the synthesizer, i.e., the sum of the coherent and incoherent scattered fields, shown in Figure 10 by the black line alongside the cyan line representing the experimentally-obtained data. All of the signal levels are normalized with respect to the corresponding signal level in free space, thus showing signal enhancement by positive values and signal attenuation by the negative values. As the UAV was changing its position in all the three \(x\), \(y\) and \(z\) axes, the signal levels are shown simply against a time axis representing time of time flight in seconds. It should be noted that the total computation time on a PC with a 3.5 GHz quad-core processor and 16 GB RAM was about 15.3 h within which the results were provided for 26,000 UAV position samples. This corresponds to about 2 s required per one calculated value, which is quite feasible respecting the size of the scenario and site-specific character of the synthesizer. At the beginning, the UAV was at the top left position in Figure 9a and it was moving in the south-southeast direction where the blockage by the tree started to occur, as indicated in Figure 9b. In Figure 10, we can identify that the attenuation by the tree starts to be pronounced after the time of 15 s. Before this time, we can identify both the slow and fast fading present in the experimental data and, mainly, the fast fading in the generated output. The fast fading due to the incoherent scattered field in the simulated output is in the order of about 3 dB in magnitude, corresponding to the experimental data set only very roughly, while the deep fades (e.g., around 11 s, 14 s, 16 s, 23 s, and 25 s) are missing. This is a consequence of the following: In the simulation, the actual tree canopies are idealistically represented by cylinders of scatterers and, thus, the blockage by individual large branches of the actual canopies is disregarded. Further, tree positions and dimensions should be obtained more precisely. More importantly, ground reflections were not included in the simulations. In addition, only the vertical polarization was addressed. Additionally, only the tree canopies, not the trunks, are considered. Despite all of this, the generated time series fit the experimentally-obtained data very closely. In the shadowed area behind the tree, the resulting attenuation obtained by the synthesizer matches the experimental data very well, respecting the diffraction pattern around the canopy.