# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 6 Optimization Process and Discussion of Simulation Results  
Computer simulations can help to determine the electrical parameters and radial characteristics of the antenna; in this case, we used FEKO software developed by the Altair company. The analysis of electrical parameters of the radiating element design solution and other antenna elements indicates that there is room to improve the electrical parameters of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Antenna Component** & **Symbol** & **Dimensions (mm)** \\ \hline Ground plane width & \(W_{\text{S}}=W_{\text{e}}\) & 13.59 \\ Ground plane length & \(L_{\text{S}}=L_{\text{e}}\) & 12.00 \\ Patch width & \(W_{\text{P}}\) & 4.17 \\ Patch length & \(L_{\text{P}}\) & 2.58 \\ Copper thickness & \(C_{\text{u}}\) & 0.05 \\ Substrate thickness & h & 1.57 \\ Permitivity & \(E_{\text{r}}\) & 2.20 \\ Feed line width & \(W_{\text{f}}\) & 3.07 \\ Feed line length & \(L_{\text{f}}\) & 4.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dimensions of the preliminary antenna design. Figure 2: Antenna model view, front and back side. the antenna by reducing VSWR, increasing the width of the operating band, miniaturizing antenna dimensions, and increasing antenna gain. For this purpose, the design was optimized for the parameters mentioned above, using FEKO software from the Altair company. The calculated parameters of the rectangular patch were used to create a preliminary simulation of the electrical parameters of the developed antenna model. Then, the model underwent a process of optimization, with the main assumption that the antenna must operate at the resonance frequency of 28 GHz. The FEKO software supports the ability to define the electrical parameters optimization process for the designed antenna and provides a number of options related to optimization. The algorithm of the optimization process used by the FEKO software is shown in Figure 3.  
During the optimization process, one of the following optimization methods of FEKO software must be chosen: simplex (Nelder-Mead), particle swarm optimization (PSO), or genetic algorithm (GA). For the above methods, there are two options for stop criteria:  
**Specify maximum number of runs by the solver:** The optimization process ends when the FEKO solver has been run a certain number of times during the optimization process. In the case of PSO and GA methods, if a full swarm or generation is not generated within the allowed number of assigned runs, optimization may be completed before the indicated number of runs by the solver. If the optimization process is terminated prematurely, due to a reduction in the number of runs by the solver, the software will provide the optimal solutions found up to that point, as well as information about the optimization process. **Optimization convergence accuracy (standard deviation):** This option allows adjustment of accuracy levels required for the optimization process. Three options for selecting the accuracy level are available, i.e., high, normal, and low. The selected accuracy level of the optimization process modifies the conditions in which the search algorithm will converge, and the effect depends on the selected method. Figure 3: The algorithm of the optimization process in FEKO software. In the optimization process of the proposed antenna, the option \"specify maximum number of runs by the solver\" was chosen. In the next step, we have to define the parameters of the antenna model that are to be changed during the optimization process in order to achieve an optimal solution. The parameters define the variables, which can be changed during the optimization process. These parameters are local for each optimization search, and a correct search must contain at least one defined parameter. Any variable defined in the FEKO software can be used as an optimization parameter, for example, the physical dimensions of the model, load, and source (amplitude and phase), provided that no relationship is implied between the optimization parameters in the same search.