# Near-Field-Based 5G Sub-6 GHz Array Antenna Diagnosis Using Transfer Learning
## 1 Introduction  
Recently, with the expansion of the 5G mobile communication business, there has been a rapid increase in the demand for both single antennas, which are used in existing mobile phones, and array antennas, which have high gain and beam steering [1, 2]. An array antenna consists of radiation elements and electronic components for controlling transmission and reception signals. It adjusts the amplitude and phase of the signal applied to each radiation element differently, and it subsequently steers the beam based on the principle of combination and cancellation of the radiation signals of the individual radiation elements [3]. In general, although an array antenna is composed of the same radiation elements and electronic components, the amplitude and phase errors of the applied signal are caused by problems such as component failure, mutual interference, and heat generation [4]. As these errors affect the performance of an array antenna, its diagnosis is essential. Array antenna diagnosis methods are primarily classified as far-field and near-field methods. A far-field diagnosis based on the three-dimensional one-point measurement method has high accuracy; however, the measurement time is long, and the economic feasibility is low. The latter is a result of the requirement of an expensive, large-scale method, such as an electromagnetic wave anechoic chamber that can provide a far-field area. A near-field diagnosis method measures the electric field at regular intervals on an arbitrary plane in a radiating near-field of the antenna to be tested. Under modal expansion, the magnitude and phase of the angular spectrum of the plane wave can be determined from the measured near field [5]. Specifically, the electric field at any position can be calculated, and this method is called the back-projection technique. If this technique is applied to the surface of an array antenna, the electric field distribution near the surface can be predicted, and the correction/error, amplitude, and phase error can be determined in units of the array antenna elements. However, this technique is effective when more than acertain amount of the energy radiated by the array antenna can be measured [6]. The back-projection technique requires measurements in a radiating near-field at a certain distance from the array antenna. Thus, a wide-area near-field measurement is required, which increases the measurement time. This means there is a trade-off relationship between the measurement time and the accuracy. In addition, because the resolution of the electric-field distribution image of an array antenna surface obtained by the back-projection technique is one wavelength interval, it is difficult to diagnose a particular antenna element when the antenna elements are dense. Therefore, it is difficult to obtain high accuracy and a short measurement time with the conventional array antenna diagnosis method [7]. To overcome these shortcomings, in this paper, we propose an array antenna diagnosis method by applying the imaged electric-field distribution to a convolutional neural network (CNN) [8]. Recently, the field of computer vision has rapidly developed with deep learning technology. In particular, the CNN method has led to breakthroughs in image recognition, classification, extraction, and tracking based on big data images, such as ImageNet [9]. Moreover, its performance has also been significantly improved, and it has been commonly used in various fields. To employ a general CNN, a developer must implement all layers directly and derive a CNN structure that is suitable for the target purpose by repeated training. However, this method requires high skills and is also inefficient in terms of the time and the computational cost. As an alternative, transfer learning [10], which trains a new model using the weights of a pre-trained deep learning model, has recently received significant attention.