Accurate localized wireless channel modeling is a cornerstone of cellular network optimization, enabling reliable prediction of network performance during parameter tuning. Localized statistical channel modeling (LSCM) is the state-of the-art channel modeling framework tailored for cellular network optimization. However, traditional LSCM methods, which infer the channel's angular power spectrum (APS) from reference signal received power (RSRP) measurements, suffer from critical limitations: they are typically confined to single-cell, single grid and single-carrier frequency analysis and fail to capture complex cross-domain interactions. To overcome these challenges, we propose RF-LSCM, a novel framework that models the channel APS by jointly representing large-scale signal attenuation and multipath components within a radiance field. RF-LSCM introduces a multi-domain LSCM formulation with a physics informed frequency-dependent attenuation model (FDAM) to facilitate the cross frequency generalization as well as a point cloud-aided environment enhanced method to enable multi-cell and multi-grid channel modeling. Furthermore, to address the computational inefficiency of typical neural radiance fields, RF LSCMleverages a low-rank tensor representation, complemented by a novel hierarchical tensor angular modeling (HiTAM) algo rithm. This efficient design significantly reduces GPU memory requirements and training time while preserving fine-grained accuracy. Extensive experiments on real-world multi-cell datasets demonstrate that RF-LSCM significantly outperforms state-of the-art methods, achieving up to a 30% reduction in mean absolute error (MAE) for coverage prediction and a 22% MAE improvement by effectively fusing multi-frequency data.

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.