Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays Using Baseband Signal | IEEE Journals & Magazine | IEEE Xplore

Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays Using Baseband Signal


Abstract:

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 cr...Show More

Abstract:

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 article 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 (SNR) 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.
Published in: IEEE Transactions on Antennas and Propagation ( Volume: 70, Issue: 7, July 2022)
Page(s): 5044 - 5053
Date of Publication: 10 June 2022

ISSN Information:

Funding Agency:


References

References is not available for this document.