## 4 Parasitic Layer Design Example
The process is illustrated for the \((\theta,\phi)=(0^{\circ},0^{\circ})\) target in Figure 14. The cost of all the possible configurations is shown in Figure 14, while the reduced set of non-dominated individuals is illustrated in Figure 14, this set represents the configurations with better performance in any of the objectives defined. Figure 14: Optimization process. Depending on the reachable cost vector for a particular target mode of operation, and whether a small mismatch, miss-pointing, or higher power going to the switches is tolerable, the designer can evaluate a subset of the configurations on the non-dominated set and determine, based on features like for instance pattern shape, impedance bandwidth, or nonlinear metrics, the configurations that better suits the application. The procedure illustrated in Figure 14, is carried out for each of the 9 beam directions intended, and for both ports. Although this process can be seen as performing 18 independent optimizations, it is very fast as cost vector is calculated only once for all the switches configurations and the remaining problem is dealt as a classification task repeated for the intended 18 configurations. Results are shown and discussed only for port 1 noting that the values obtained for port 2 are very similar. Once the better performing configurations are found, these are validated by simulation with CST Design Studio, finding an almost exact match. Similarly, the port parameters calculated using the measured S- and X-parameters models of the loads are verified to be in a very good agreement. The final configuration of the antenna have a satisfactory performance characterized by an aggregated bandwidth of 10.5% centered at the intended \(f_{c}=3.5\) GHz. Additionally, the optimum configurations providing beam-steering, denoted as \(M_{1-9}\), are well matched and isolated at the central frequency, albeit slight shifting of minimum is allowed as the balancing criteria privileges accurate pointing over port impedance. The port match and isolation at \(f_{c}\) are summarized in Table 2.  
On the other hand, the power margin for the optimum configurations are (13.37, 5.50, 7.66, 7.75, 6.98, 5.52, 7.04, 7.66, 6.94) dB, demonstrating a mean value of 8.34 dB, what is a 6 dB improvement when compared with a design strategy that only considers port match and radiation pattern characteristics, and is in accordance to the expected value from the design guidelines, this means also that a small fraction of the power entering the antenna is dissipated on switches and thus a low nonlinearity is expected. Likewise, Figure 15 shows the radiation diagrams for the final antenna configurations for port 1. From this figure, a very good pointing towards the target angles defined is observed. Also, a mean gain of 6.13 dB is found which is in accordance with previous PL designs. Finally, the IP3 and further EVM nonlinear metrics of the antenna optimum configurations are calculated using the diode nonlinear diode models extracted by X-parameters measurements. Results for EVM are illustrated in Figure 16 showing that up to 30 dBm the EVM is below 6% for all the antenna states, and that up to 35 dBm the mean EVM is below 4%. In comparison with a design strategy that only considers port match and radiation pattern characteristics, where EVM values up to 20% are observed, this is a 12 dB improvement. Note that the modes with the lower power margin present the higher EVM as expected from the design assumptions. This result confirms the suitability of this parasitic-layer-based reconfigurable-antenna design for 5G NR mobile broadband systems, and at the same time shows the benefits of the new design approach considering the switches power to minimize the nonlinear distortion.