Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays using Baseband Signal
This addresses the need for efficient and timely fault diagnosis in communication systems, offering a practical solution for on-site deployment, though it is incremental as it applies existing DNN techniques to a specific domain problem.
The paper tackles the problem of on-site fault diagnosis in mm-wave active phased arrays for 5G/6G radios by proposing a deep neural network method that uses baseband signals, achieving 99% accuracy for single-element failures and 80% for multi-element failures with detection times as low as 6 ms.
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.