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

2025-09-16

AI Summary

Local statistical channel modeling (LSCM) traditionally relies on costly, low-coverage drive-test data, limiting scalability and practicality. To address this, this paper proposes an end-to-end modeling framework leveraging massive, low-cost measurement reports (MRs). The core innovation is a distance-aware hypergraph neural network that jointly performs semi-supervised localization and angular power spectrum estimation. By integrating hypergraph convolution, adaptive grid-based clustering, and an improved sparse recovery scheme, the method effectively tackles two key challenges: location imputation under non-uniform sampling and ill-posed matrix inversion in channel parameter estimation. Evaluated on real-world MR datasets, the framework achieves a 32% reduction in average localization error and significantly improves statistical channel modeling accuracy. It further demonstrates high computational efficiency and strong robustness against data sparsity and noise. This work establishes a novel, lightweight paradigm for channel sensing in digital twin networks.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Local Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Localized statistical channel modeling (LSCM) is crucial for effective performance evaluation in digital twin-assisted network optimization. Solely relying on the multi-beam reference signal receiving power (RSRP), LSCM aims to model the localized statistical propagation environment by estimating the channel angular power spectrum (APS). However, existing methods rely heavily on drive test data with high collection costs and limited spatial coverage. In this paper, we propose a measurement report (MR) data-driven framework for LSCM, exploiting the low-cost and extensive collection of MR data. The framework comprises two novel modules. The MR localization module addresses the issue of missing locations in MR data by introducing a semi-supervised method based on hypergraph neural networks, which exploits multi-modal information via distance-aware hypergraph modeling and hypergraph convolution for location extraction. To enhance the computational efficiency and solution robustness, LSCM operates at the grid level. Compared to independently constructing geographically uniform grids and estimating channel APS, the joint grid construction and channel APS estimation module enhances robustness in complex environments with spatially non-uniform data by exploiting their correlation. This module alternately optimizes grid partitioning and APS estimation using clustering and improved sparse recovery for the ill-conditioned measurement matrix and incomplete observations. Through comprehensive experiments on a real-world MR dataset, we demonstrate the superior performance and robustness of our framework in localization and channel modeling.

Problem

• Estimating channel angular power spectrum from limited RSRP measurements
• Addressing missing location data in measurement reports for localization
• Jointly optimizing grid partitioning and APS estimation in complex environments

Innovation

• Semi-supervised hypergraph neural networks for MR localization
• Joint grid construction and APS estimation via clustering
• Improved sparse recovery for ill-conditioned measurement matrices