Title: A Measurement Report Data-Driven Framework for Localized Statistical Channel Modeling Show Chinese title

Title: 一种用于局部统计信道建模的测量报告数据驱动框架

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. Show Chinese abstract

Abstract: 局部化统计信道建模（LSCM）对于数字孪生辅助的网络优化中的有效性能评估至关重要。 仅依赖多波束参考信号接收功率（RSRP），LSCM旨在通过估计信道角度功率谱（APS）来建模局部化的统计传播环境。 然而，现有方法严重依赖于成本高且空间覆盖有限的路测数据。 在本文中，我们提出了一种基于测量报告（MR）数据的框架用于LSCM，利用了MR数据低成本和广泛收集的特点。 该框架包含两个新模块。 MR定位模块通过引入基于超图神经网络的半监督方法，解决了MR数据中缺失位置的问题，该方法通过距离感知的超图建模和超图卷积来利用多模态信息进行位置提取。 为了提高计算效率和解决方案的鲁棒性，LSCM在网格级别运行。 与独立构建地理均匀网格并估计信道APS相比，联合网格构建和信道APS估计模块通过利用它们的相关性，在具有空间非均匀数据的复杂环境中增强了鲁棒性。 该模块通过聚类和改进的稀疏恢复来交替优化网格划分和APS估计，针对病态测量矩阵和不完整观测。 通过在真实世界MR数据集上的全面实验，我们证明了我们的框架在定位和信道建模方面的优越性能和鲁棒性。