Spatially Consistent Air-to-Ground Channel Modeling and Simulation via 3D Shadow Projections

TL;DR

The paper introduces a fast, spatially consistent semi-deterministic A2G channel modeling framework that uses geometry-based shadow projections (GBSP) to compute LOS maps in 3D urban environments. It combines LOS-aware deterministic path loss with spatially correlated shadow fading to generate mobility-aware radio maps, implemented in the ISCA2G simulator and validated on ITU-defined Manhattan grids. The results show realistic LOS/NLOS transitions and outage behavior across suburban to high-rise urban environments, with significantly lower computational cost than full ray tracing. This approach provides an efficient alternative for UAV-based networks and 6G NTN planning and radio-map generation, enabling environment- and mobility-aware performance evaluation. The work lays a foundation for integrating CKMs and GIS data for real-world deployments and optimization tasks.

Abstract

We present an approach for spatially-consistent semi-deterministic Air-to-Ground (A2G) channel modeling in Unmanned Aerial Vehicle-assisted networks. We use efficient 3D building shadow projections to determine Line-of-Sight (LOS) regions, enabling fast generation of LOS maps. By integrating LOS-aware deterministic path loss with stochastic shadow fading, the approach produces spatially consistent A2G radio maps suitable for environment- and mobility-aware channel evaluation and performance prediction. Simulation results in ITU-compliant Manhattan grid environments demonstrate the model's ability to reflect key urban propagation characteristics, such as LOS blockage patterns and outage behavior. The proposed approach provides an efficient alternative to ray tracing or fully stochastic models, with particular relevance for user mobility, link planning, and radio map generation in 6G non-terrestrial networks.

Figures (5)

• Figure 1: An example scenario of a system model depicting an $n-$th building described by $v_n=5$ vertices and the resulting shadow region $\mathbf{S}_n$. The dotted line indicates the user route $\mathbf{R}$ containing LOS and NLOS segments shown in blue and red, respectively.
• Figure 2: Example simulation (top view): ABS is deployed at 120 meters height in the Manhattan urban environment. The route is partly affected by shadows cast by square buildings placed as in ITU.
• Figure 3: CDF of simulated NLOS (top) and LOS (bottom) distances in different environments. More sparse environments cause shorter NLOS and longer LOS segments. The shape of distributions is influenced by the regular layout and exhibits a relation to $W$ and $St$.
• Figure 4: CDF of channel composed on path loss and shadow fading. Outage probability can be estimated based on the intersection of the CDFs with $\Lambda_{\text{outage}}$ allowed by the receiver sensitivity and the transmitter EIRP.
• Figure 5: CDF of outage distances in Suburban and High-Rise Urban environments. The difference between the outage distances Urban, Dense Urban, and High-Rise Urban environments is negligible and we omit those curves.