The millimeter-wave (mmWave) antenna array plays an important role in the excellent\nperformance of wireless sensors networks (WSN) or unmanned aerial vehicle (UAV) clusters.\nHowever, the array elements are easily damaged in its harsh working environment but hard to\nbe repaired or exchanged timely, resulting in a serious decline in the beamforming performance.\nThus, accurate self-diagnosis of the failed elements is of great importance. In previous studies,\nthere are still significant difficulties for large-scale arrays under extremely low SNR. In this paper,\na diagnosis algorithm with low complexity and high reliability for the failed elements is proposed,\nwhich is based on a joint decision of communication signal and sensing echoes. Compared with the\nprevious studies, the complexity of the algorithm is reduced by the construction of low-dimensional\nfeature vectors for classification, the decoupling of the degree of arrival (DOA) estimation and the\nfailed pattern diagnosis, with the help of the sub-array division. Simulation results show that, under\nan ultra-low SNR of -12.5 dB for communication signals and -16 dB for sensing echoes, an accurate\nself-diagnosis with a block error rate lower than 8% can be realized. The study in this paper will\neffectively promote the long-term and reliable operation of the mmWave antenna array in WSN, UAV\nclusters and other similar fields.
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