EMPC-RIS: Failure Diagnosis Method for Reconfigurable Intelligent Surfaces Based On Electromagnetic Physical Constraints

Reconfigurable Intelligent Surfaces (RIS), as the core technology for 6G wireless communications and Integrated Sensing and Communication (ISAC), play a vital role in enhancing signal quality and improving communication efficiency. To address the critical issue of damaged or contaminated RIS elements during design and deployment, this paper proposes a failure diagnosis method based on Electromagnetic Physical Constraints (EMPC). First, a low-dimensional measurement matrix is generated via spatial encoding of sparse signals from impaired RIS elements, subject to physical constraints imposed by the RF device structure. Subsequently, to accurately model the multi-modal characteristics of signal distributions in multipath channels, a Scaled Gaussian Mixture (SMG) model is adopted to fit the distribution of sparse coefficients. Leveraging the spatial structural correlation within the RIS array, a center-oriented parameter propagation acceleration strategy is designed, significantly enhancing computational efficiency. Building upon this foundation, the model parameters of the RIS are mapped onto a Riemannian manifold using Information Geometry (IG) theory. This transforms the fault diagnosis problem into one of measuring the information distance between coordinates on the manifold. A fast Kullback-Leibler Divergence (KLD)-based difference detector is designed to rapidly diagnose the locations and quantity of failed RIS elements. The experimental evaluation comprehensively validates the method's performance through extensive numerical simulations (encompassing noise impacts, device parameters, diverse fault modes, and channel models) and practical measurements in a microwave anechoic chamber. The results are analyzed from multiple dimensions to discusss the method's effectiveness, robustness, and practical applicability.