SABERChou, Shih-Kai (Avtor) Zhao, Mengran (Avtor) Hu, Cheng-Nan (Avtor) Chou, Kuang-Chung (Avtor) Fortuna, Carolina (Avtor) Hribar, Jernej (Avtor) angle of arrivalestimationreconfigurable intelligent surfacesymbolic regressionAccurate angle-of-arrival (AoA) estimation is essential for next-generation wireless communication systems to enable reliable beamforming, high-precision localization, and integrated sensing. Unfortunately, classical high-resolution techniques require multielement arrays and extensive snapshot collection, while generic machine-learning (ML) approaches often yield black-box models that lack physical interpretability. To address these limitations, we propose a symbolic regression (SR)-based ML framework. Namely, symbolic regression-based angle of arrival and beam pattern estimator (SABER), a constrained SR framework that automatically discovers closed-form beam pattern and AoA models from path loss measurements with interpretability. SABER achieves high accuracy while bridging the gap between opaque ML methods and interpretable physics-driven estimators. First, we validate our approach in a controlled free-space anechoic chamber, showing that both direct inversion of the known cos[sup]n beam and a low-order polynomial surrogate achieve sub-0.5° mean absolute error (MAE). A purely unconstrained SR method can further reduce the error of the predicted angles, but produces complex formulas that lack physical insight. Then, we implement the same SR-learned inversions in a real-world, reconfigurable intelligent surface (RIS)-aided indoor testbed. SABER and unconstrained SR models accurately recover the true AoA with near-zero error. Finally, we benchmark SABER against the Cramér–Rao lower bounds (CRLBs). Our results demonstrate that SABER is an interpretable and accurate alternative to state-of-the-art and black-box ML-based methods for AoA estimation.IEEE20262026-05-11 13:14:35Neznano29348ZDAsl© 2026 The Authors.