# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 3 Experiments with Simulation-Based Data
### Regression of Excitation Signal Amplitude and Phase of Array Antenna  
The assumption of failure of each port for the diagnosis of the 4 \(\times\) 4 array antenna is very difficult to reflect in reality, owing to the numerous combinations. Therefore, directly predicting the amplitude and phase of the excitation signal applied to each array antenna port is an understandable solution for diagnosing the array antenna. For this, training was conducted by implementing a regression network for predicting the amplitude and phase of the excitation signal applied to each port. This regression network predicts excitation amplitudes and phases for 16 ports, i.e., values for a total of 32 variables. The extraction environment of the near-field data was similar to that in the classification case. Signals with a random amplitude between 0 and 1 and a random phase between 0 and 360\({}^{\circ}\) were excited to 16 ports. Examples of extracted near-field data are shown in Figure 5.  
Figure 4: Dataset configuration of specific port failure diagnosis. A total of 30,000 data were extracted by linking CST MWS and MATLAB. Table 6 lists the accuracies obtained using the test data in the trained regression network as the epoch progresses. Here, accuracy refers to the proportion of the predicted values that are within \(\pm\)5% of the true values. When a total of 9000 epochs are performed, the training time is 7 days, 16 h, 27 min, and 17 s. The accuracies for amplitude and phase almost converge from 7000 epochs, and accuracies of approximately 95% and 98% are achieved for the excited amplitude and the excited phase, respectively. In order to check the performance of the trained network, the images of cases 1 and 2 shown in Figure 5c,d were applied to the network to predict the amplitude and phase of the signal. Table 7 is a comparison between the true values and the predicted values of cases 1 and 2 applied to the trained regression network. It can be seen that the predicted values are close to the true values. Thus, using this transfer learning method will lead to accurate diagnosis and calibration of array antennas. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Epochs**} \\ \cline{2-9}  & & **4000** & **5000** & **6000** & **7000** & **8000** & **9000** \\ \hline \multirow{2}{*}{**Accuracy (\%)**} & **Amp.** & 85.61 & 88.21 & 91.20 & 95.68 & 96.30 & 96.20 \\ \cline{2-9}  & **Phase** & 92.69 & 94.21 & 96.33 & 98.30 & 98.97 & 98.65 \\ \hline \multirow{2}{*}{**Execution time**} & 3 d 8 h & 4 d 05 h & 5 d 1 h & 5 d 22 h & 6 d 19 h & 7 d 16 h \\  & 53 m 21 s & 36 m 24 s & 43 m 03 s & 33 m 13 s & 33 m 10 s & 27 m 17 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training results for the excited signal prediction regression network (6000 test data). Figure 5: Examples of near-field data based on random excited signal: (**a**) excitation amplitude of ports for cases 1 and 2, (**b**) excitation phase of ports for cases 1 and 2, (**c**) near-field distribution in case 1, and (**d**) near-field distribution in case 2. Regression Using Sampling Line of E-Field Data to Reduce the Number of Near-Field Measurement Points  
In this study, array antenna diagnosis by transfer learning was conducted by extracting near-field data from the area surrounding the array antenna at a distance of \(1\lambda\) from a distance of \(3\lambda\) above the array antenna. However, actual measurements of the near field of the corresponding area require at least several minutes to several tens of minutes. Although the measurement time can be shortened by mechanical improvement of the scanner, there are limitations if the area to be measured is wide. Therefore, we aim to implement a neural network that can diagnose an array antenna using only a few sampling lines without performing near-field measurements for the entire area. As shown in Figure 6, only e-fields of a total of 14 lines were sampled from those of the \(34\times 34\) points used in the present training.