One key communication block in 5G and 6G radios is the active phased array (APA). To ensure reliable operation, efficient and timely fault diagnosis of APAs on-site is crucial. To date, fault diagnosis has relied on measurement of frequency domain radiation patterns using costly equipment and multiple strictly controlled measurement probes, which are time-consuming, complex, and therefore infeasible for on-site deployment. This paper proposes a novel method exploiting a Deep Neural Network (DNN) tailored to extract the features hidden in the baseband in-phase and quadrature signals for classifying the different faults. It requires only a single probe in one measurement point for fast and accurate diagnosis of the faulty elements and components in APAs. Validation of the proposed method is done using a commercial 28 GHz APA. Accuracies of 99% and 80% have been demonstrated for single- and multi-element failure detection, respectively. Three different test scenarios are investigated: on-off antenna elements, phase variations, and magnitude attenuation variations. In a low signal to noise ratio of 4 dB, stable fault detection accuracy above 90% is maintained. This is all achieved with a detection time of milliseconds (e.g 6~ms), showing a high potential for on-site deployment.

翻译：第五代（5G）与第六代（6G）无线通信系统中的关键通信模块之一是有源相控阵（APA）。为确保其可靠运行，对APA进行高效且及时的现场故障诊断至关重要。迄今为止，故障诊断依赖使用昂贵设备及多个严格控制的测量探针来测量频域辐射方向图，该方法耗时、复杂，因此不适用于现场部署。本文提出一种新颖方法，利用专门提取基带同相与正交信号中隐藏特征的深度神经网络（DNN），对各类故障进行分类。该方法仅需在单一测量点使用单个探针，即可快速准确地诊断APA中的故障单元与组件。通过商用28 GHz APA对所述方法进行了验证：在单元件与多元件故障检测中，分别实现了99%和80%的准确率。研究考察了三种测试场景：天线单元通断、相位变化以及幅度衰减变化。在低至4 dB的信噪比条件下，故障检测准确率仍稳定维持在90%以上。所有检测均在毫秒级时间内（如6毫秒）完成，展现出极高的现场部署潜力。