A Geometry Map-Based Site-Specific Propagation Channel Model for Urban Scenarios

TL;DR

This paper introduces a geometry map-based urban propagation model that accurately predicts site-specific path loss and Doppler effects by leveraging 3D maps and UTD, outperforming existing models.

Contribution

It presents a novel geometry map-based model with an efficient building detection algorithm, improving accuracy in urban radio propagation predictions over traditional models.

Findings

Outperforms 3GPP and simplified models in NLOS scenarios, reducing RMSE by 7.1 dB and 3.18 dB.

Accurately predicts large-scale path loss in LOS and NLOS conditions.

Effectively captures time-varying Doppler characteristics in urban environments.

Abstract

With the rapid deployments of 5G and 6G networks, accurate modeling of urban radio propagation has become critical for system design and network planning. However, conventional statistical or empirical models fail to fully capture the influence of detailed geometric features on site-specific channel variances in dense urban environments. In this paper, we propose a geometry map-based propagation channel model that directly extracts key parameters from a 3D geometry map and incorporates the Uniform Theory of Diffraction (UTD) to recursively compute multiple diffraction fields, thereby enabling accurate prediction of site-specific large-scale path loss and time-varying Doppler characteristics in urban scenarios. A well-designed identification algorithm is developed to efficiently detect buildings that significantly affect signal propagation. The proposed model is validated using urban measurement data, showing excellent agreement of path loss in both line-of-sight (LOS) and nonline-of-sight (NLOS) conditions. In particular, for NLOS scenarios with complex diffractions, it outperforms the 3GPP and simplified models, reducing the RMSE by 7.1 dB and 3.18 dB, respectively. Doppler analysis further demonstrates its accuracy in capturing time-varying propagation characteristics, confirming the scalability and generalization of the model in urban environments.