# 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 data were reconstructed into a \(14\times 34\) array, and regression was performed on the excited amplitude and phase of the \(4\times 4\) array antenna. Similar to using the near field of the entire area, signals with a random amplitude between 0 and 1 and a random phase of 0 to \(360^{\circ}\) were excited to 16 ports. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Case 1**} & \multicolumn{6}{c}{**Case 2**} \\ \cline{2-10}
**Port Number** & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{**Phase (deg)**} & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{**Phase (deg)**} \\ \cline{2-10}  & **True** & **Pred. ** & **True** & **Pred. ** & **True** & **Pred. ** & **True** & **Pred. ** \\ \hline
1 & 0.74 & 0.78 & 268 & 274 & 0.97 & 0.93 & 56 & 56 \\ \hline
2 & 0.38 & 0.43 & 202 & 198 & 0.93 & 0.91 & 110 & 120 \\ \hline  
A total of 30,000 data were used in the regression network, which is similar to the case of using the entire area. Table 8 compares the accuracies obtained using the test data in the trained regression network using a sampling line as the epoch progresses. When a total of 15,000 epochs are performed, the training time is 9 days, 21 h, 41 min, and 49 s. The accuracies almost converge from 13,000 epochs onward, and accuracies of approximately 91% and 96% are achieved for the excited amplitude and the excited phase, respectively. Although the number of near-field points is reduced by more than 58% from the previous \(34\times 34\) (1156 points) to \(14\times 34\) (476 points), more than 91% accuracy is attained in the prediction of the excited signal of the array antenna. Table 9 lists the results of the regression network trained in the same environment as above using Resnet-50 as the pre-trained network for comparison according to the type of pre-trained network. When Resnet-50, which is more stable than Alexnet, is used as the pre-trained network, the convergence of the network accuracy occurs in a shorter time than with Alexnet. However, using Alexnet as the pre-trained network leads to slightly higher accuracy than Resnet-50. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Epochs**} \\ \cline{3-7}  & & **5000** & **7000** & **9000** & **11,000** & **13,000** & **15,000** \\ \hline \multirow{2}{*}{**Accuracy (\%)**} & **Amp.** & 63.58 & 72.49 & 77.88 & 83.91 & 91.01 & 90.56 \\ \cline{2-7}  & **Phase** & 70.63 & 81.26 & 85.54 & 91.08 & 96.22 & 95.97 \\ \hline \multirow{2}{*}{**Execution time**} & 3 d 14 h & 4 d 23 h & 5 d 16 h & 7 d 02 h & 8 d 12 h & 9 d 21 h \\  & 0 m 36 s & 33 m 21 s & 58 m 55 s & 29 m 38 s & 06 m 41 s & 41 m 49 s \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Training results for excited signal prediction regression network using sampling line data (6000 test data, pre-trained network: Alexnet). Figure 6: Position of sampling line in the near field. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Epochs**} \\ \cline{3-6}  & & **1000** & **1500** & **2000** & **2500** \\ \hline \multirow{2}{*}{**Accuracy (\%)**} & **Amp.** & 81.28 & 92.82 & 91.05 & 92.12 \\ \cline{2-6}  & **Phase** & 79.8 & 93.47 & 91.91 & 92.54 \\ \hline \multirow{2}{*}{**Execution time**} & 3 d 11 h & 5 d 04 h & 6 d 22 h & 8 d 15 h \\  & 06 m 19 s & 30 m 43 s & 07 m 24 s & 21 m 54 s \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Training results for excited signal prediction regression network using sampling line data (6000 test data, pre-trained network: Resnet-50).