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

• URL: http://arxiv.org/abs/2306.04360v1
• Date: Wed, 7 Jun 2023 11:46:14 GMT
• Title: Robust and Efficient Fault Diagnosis of mm-Wave Active Phased Arrays using Baseband Signal
• Authors: Martin H. Nielsen, Yufeng Zhang, Changbin Xue, Jian Ren, Yingzeng Yin, Ming Shen, and Gert F. Pedersen
• Abstract summary: One key communication block in 5G and 6G radios is the active phased array (APA) 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.
• Score: 10.64967731507698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

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