  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.
