Empirical Line-of-Sight Probability Modeling for UAVs in Random Urban Layouts

TL;DR

The paper addresses the limitation of ITU Manhattan-based $P_{LoS}$ models in truly random urban environments by introducing the Urban LoS Simulator (ULS), which generates three layouts (Random-Manhattan, Random-Urban, Random-Highway) using ITU built-up parameters and derives empirical $P_{LoS}(\theta)$ curves for four urban environments. It compares two sigmoid fits, finding $Sig_2$ provides better accuracy, and shows that RU layout best matches real-world residential LoS behavior (e.g., Cologne WI data with RMSE $0.0699$, MAE $0.0540$, $R^2=0.952$), while Manhattan-based models remain reasonably predictive under the same parameters. The work demonstrates that building height dominates LoS probability more than building area and provides a practical, layout-aware method for evaluating UAV-based ABS performance in time-critical urban IoT networks; the ULS is slated for public release. Overall, the study highlights the value of incorporating randomness in city layouts to improve $P_{LoS}$ estimation and informs UAV deployment strategies in diverse urban morphologies.

Abstract

Accurate Probability of Line-of-Sight (PLoS) modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing PLoS models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined built-up parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban LoS Simulator (ULS) to model PLoS for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical PLoS for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate PLoS results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

Figures (7)

• Figure 1: Top view of the proposed layouts in ULS for the standard urban environment ($\alpha = 0.3, \beta = 500, \gamma = 15$).
• Figure 2: $Sig_1$ and $Sig_2$ fitting comparison.
• Figure 3: Simulated and empirical $P_{LoS}(\theta)$ curves for the four standard urban environments using different layouts.
• Figure 4: $P_{LoS}$ comparison of Manhattan-based models and simulated layouts in the urban environment.
• Figure 5: $P_{LoS}$ Comparison for road and street users in RH-based urban environment.