# A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling

arXiv: https://arxiv.org/abs/2303.02308 · HTML: https://mlsanity.com/paper/2303.02308/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2023.

## Built on by (most cited first, 7 of 7)

- [A Disentangled Representation Learning Framework for Low-altitude Network Coverage Prediction](https://mlsanity.com/paper/2507.14186/) (2025)
- [U6G XL-MIMO Radiomap Prediction: Multi-Config Dataset and Beam Map Approach](https://mlsanity.com/paper/2603.06401/) (2026)
- [Point-Cloud-Assistant Localized Statistical Channel Prediction by Tangent Gaussian Splatting](https://mlsanity.com/paper/2606.18734/) (2026)
- [Learning to Gridize: Segment Physical World by Wireless Communication Channel](https://mlsanity.com/paper/2507.15386/) (2025)
- [Neural Beam Field for Spatial Beam RSRP Prediction](https://mlsanity.com/paper/2508.06956/) (2025)
- [RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization](https://mlsanity.com/paper/2509.13686/) (2025)
- [Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning](https://mlsanity.com/paper/2609.32836/) (2026)

## Abstract (arXiv)

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel's angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths' powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method.