Scale-Robust Blind Antenna Fault Diagnosis for Hybrid Beamforming Systems

Antenna fault diagnosis is critical to the operational reliability of massive multiple-input multiple-output (MIMO) systems. However, conventional diagnostic frameworks suffer from severe service interruption and are highly vulnerable to the channel state information (CSI) scale ambiguity, a notorious impairment typically introduced when utilizing the outputs of blind channel estimation. To circumvent these impediments, this letter proposes a scale-robust blind antenna fault diagnosis framework tailored for hybrid beamforming (HBF) architectures. Specifically, by actively harvesting the second-order statistics of uplink payload data, a non-disruptive passive monitoring paradigm is established to asymptotically suppress user symbol perturbations without dedicated diagnostic pilots. Furthermore, to address the accompanied scale uncertainty, an orthogonal subspace projection operator is developed to geometrically decouple the dominant healthy background energy from the subtle fault signatures, thereby completely neutralizing the unknown scale factors regardless of the upstream channel estimation paradigm. The resultant diagnostic optimization problem is rigorously formulated and solved via semidefinite relaxation. Simulations demonstrate that the proposed framework achieves high-accuracy fault localization under blind scale factors and outperforms existing benchmarks.