## 4 Simulation Results
The symmetrical and non-symmetrical distributions generate similar patterns, and the SLL values change only slightly, although symmetrical distributions have the advantage of reducing hardware complexity. Figure 4: Fitness function during the optimization: (**a**) N = 6, (**b**) N = 9, and (**c**) N = 12. Figure 5: Optimization variable distributions: (**a**) N = 6, (**b**) N = 9, and (**c**) N = 12. Figure 6: Radiation of a VAA with N = 6: (**a**) random without symmetry, (**b**) random with symmetry, and (**c**) Hamming S\({}_{11}\) parameter of the antenna element. Figure 7: Radiation of a VAA with N = 9: (**a**) random without symmetry, (**b**) random with symmetry, and (**c**) Hamming distribution. Table 2 contains a comparison with previous studies in the field of virtual antenna arrays. The main contribution of this work is the use of frequency diversity in virtual antenna arrays for 5G-FANETs. Moreover, most previous works used isotropic and dipole antennas with no drone structures. However, exploring the scenario of real antennas mounted on a drone is very important before real experimentation. Furthermore, other works utilized patch antennas at a frequency of 2.4 GHz. Here, the designs utilized the recently opened band of 3.5 GHz. Additionally, the previous papers designed virtual arrays with other techniques such as time modulation or only random positions. Previously, frequency diversity was not used in virtual arrays. As such, our contribution represents an advance in this topic, which permits the topic to mature. The frequency diversity in virtual antenna arrays may permit using a FANET as a radar system in future emerging applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **Array Topology** & \begin{tabular}{c} **Type of** \\ **Antenna** \\ \end{tabular} & **Frequency** & **Drone** & **Algorithm** & **Perturbations** \\ \hline Ref. [5] & \begin{tabular}{c} 3D polyhedral and \\ linear \\ \end{tabular} & Isotropic & Not included & Not included & DOA & Not included \\ \hline Ref. [6] & \begin{tabular}{c} 3D random \\ layers \\ \end{tabular} & Isotropic & Not included & Not included & DEMO & Not included \\ \hline Ref. [7] & \begin{tabular}{c} 3D random in 4 \\ layers \\ \end{tabular} & Isotropic & Not included & Not included & Not included & Not included \\ \hline Ref. [17] & \
\begin{tabular}{c} Uniform linear and \\ square with time \\ modulation \\ \end{tabular} & Dipole & Not included & Not included & PSO & Not included \\ \hline Ref. [9] & Non-uniform linear & Isotropic & Not included & Not included & Deterministic & Not included \\ \hline Ref. [10] &
\begin{tabular}{c} 3D random \\ layers \\ \end{tabular} & Isotropic & Not included & Not included & DPINSGA-II & Not included \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with virtual arrays. Figure 8: Radiation of a VAA with N = 12: (**a**) random without symmetry, (**b**) random with symmetry, and (**c**) Hamming distribution. Finally, it is important to compare this study with previous papers in the field of FDAs. In this case, Table 3 compares the most representative works in this field. Most of these works present FDAs with linear topologies and isotropic antennas. The frequency offsets are Hamming, logarithmic, and random distributions. The main difference concerning this work is the combination of random frequency offsets and non-uniform antenna locations, the 5G frequency band, and the use of elliptic patch antennas mounted on real drones.