A Physics-Based and Data-Driven Approach for Localized Statistical Channel Modeling

Localized channel modeling is crucial for offline performance optimization of wireless networks, but existing channel models are not well suited for wireless network optimization. In this paper, we propose a physics-based and data-driven localized statistical channel model for wireless network optimization. The proposed channel modeling solely relies on the reference signal receiving power (RSRP). The key is to build the statistical relationship between the RSRP and the angular power spectrum (APS). Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the APS indicate the channel paths' powers and angles of departure. Although such problem typically can be handled by orthogonal matching pursuit (OMP)-type algorithms, our problem is more challenging due to the non-uniform and closely parallel columns of the coefficient matrix. To address these issues, we propose the weighted non-negative OMP (WNOMP) and the second-order-statistics-based WNOMP (SWOMP) algorithms. The WNOMP algorithm can alleviate the effect of non-uniform columns, while the SWOMP algorithm can further identify the closely parallel columns correctly. Finally, comprehensive experiments based on synthetic and real-world RSRP are presented to demonstrate that the proposed methods outperform classic methods in terms of accuracy and mean absolute error (MAE).