A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling

Localized statistical channel modeling (LSCM), a key enabler for digital twin networks, traditionally relies on costly and spatially limited drive test data to estimate the channel angular power spectrum (APS) from reference signal received power measurements. This paper proposes a measurement report (MR) data-driven LSCM framework (MR-LSCM) to leverage low-cost and ubiquitous MR data. However, integrating MR data presents critical challenges: the prevalent lack of location labels required for LSCM, and the mismatch between uniform geographic grids in LSCM and spatially non-uniform MR data in complex propagation environments. To address these issues, our MR-LSCM framework introduces two specialized modules. First, a semi-supervised hypergraph neural network is proposed for MR localization, which exploits multimodal information to achieve robust performance even with scarce labels. Second, we unify grid construction and APS estimation into a joint clustering and sparse recovery problem where the two tasks mutually reinforce each other. An improved sparse recovery algorithm tailored to the ill-conditioned measurement matrix and incomplete observation is developed by incorporating physical priors. Through comprehensive experiments on a real-world MR dataset, we demonstrate the superior performance and robustness of our framework in localization and channel modeling.