# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 4 Validation of Diagnosis Network Using Measurements  
Measurements were conducted to validate the proposed near-field-based array antenna diagnosis using transfer learning. The tested array antenna and the measurement environment are shown in Figure 7a,b, respectively. Figure 7c shows the reflection coefficient characteristics of the tested \(4\times 4\) array antenna. It consists of four \(1\times 4\) series-fed array antennas, which are designed and manufactured to operate in the 5G sub-6 GHz band at 3.55 GHz. Figure 7d displays the shape and S-parameter characteristics of the \(1\times 4\) power divider for feeding the tested antenna. It is a Wilkinson power divider, and examination of the S-parameter characteristics shows that it distributes the same power of approximately \(-7.1\) dB at 3.55 GHz to the four ports, and the phases of all ports are similar. For verifying the performance of the fabricated array antenna by near-field measurements, four output ports of the \(1\times 4\) power divider and four input ports of the antenna were connected using cables. As the near-field probe, a dielectric rod waveguide probe with low-scattering characteristics was used [17]. The measurement area was \(370\times 370\) mm\({}^{2}\), the sampling interval was \(10\) mm, and the total sampling point was \(37\times 37\). To examine the accuracy of the measurements by the near-field probe, a uniform amplitude and phase were supplied to the four input ports of the array antenna, and the measurement was made assuming normal operation. The measured and simulated results are shown in Figure 8. Noticeably, the simulation and measurement distributions are similar.  
Figure 7: (**a**) Configuration of tested array antenna and measurement information, (**b**) measurement environment, (**c**) measured reflection coefficients of array antenna, and (**d**) measured S-parameters of \(1\times 4\) Wilkinson power divider.  
Different from the 16-port antenna analyzed by transfer learning, the tested array antenna consists of a \(1\times 4\) series feed antenna and is implemented with four ports. Implementation of an array antenna with 16 ports would exponentially increase the number of failure scenarios for each port, making verification by measurement difficult; therefore, an array antenna with four input ports is used. Training for the array antenna diagnosis by measurements was conducted separately. Classes were categorized for a total of 16 cases, assuming normal/abnormal operation for each of the four ports, and training was conducted using 1000 data for each class in the similar environment as in the previous classification. Finally, 98.13% accuracy was achieved with 3200 simulation-based test data. By measuring under various scenarios of array antenna feeding, array antenna diagnosis using a trained network was verified. The scenarios for array antenna diagnosis were assumed by terminating or connecting cables to each input port of the array antenna. The classification of the measurement condition is shown in Table 10. A total of 30 measurements were performed, and the trained network succeeded in discriminating 29 cases and showed an accuracy of 96.67%. The details of the classification network for measurement-based verification are shown in Table 11. These results verify that array antenna diagnosis by transfer learning is feasible.  
Figure 8: Near-field distribution when applying uniform excitation to ports: (**a**) simulated amplitude, (**b**) simulated phase, (**c**) measured amplitude, and (**d**) measured phase.