A Multi-Frequency Investigation of Air-to-Ground Urban Propagation Using a GPU-Based Ray Launching Algorithm

Abstract

Unmanned Aerial Vehicles (UAV), also known as "drones", are attracting increasing attention as enablers for many technical applications and services, and this trend is likely to continue in the next future. When compared to conventional terrestrial communications, those making use of UAVs as base-or relaystations can definitely be more useful and flexible in reaction to specific events, like natural disasters and terrorist attacks. Among the many and different fields, UAV enabled communications emerge as one of the most promising solutions for next-generation mobile networks, with a special focus on the extension of coverage and capacity of mobile radio networks. Motivated by the air-to-ground (A2G) propagation conditions which are likely to be different than those experienced by traditional ground communication systems, this paper aims at investigating the narrowband properties of the air-to-ground channel for 5G communications and beyond by means of GPU accelerated ray launching simulations. Line of sight probability as well as path loss exponent and shadowing standard deviations are analysed for different UAV flight levels, frequencies and dense urban scenarios, and for different types of on board antennas. Thanks to the flexibility of the ray approach, the role played by the different electromagnetic interactions, namely reflection, diffraction and diffuse scattering, in the air-to-ground propagation process is also investigated. Computation time is reported as well to show that designing UAV communication networks and optimising their performances in a fast and reliable manner, might avoid exhausting -multiple -measurement campaigns.

Air-to-Ground Large-Scale Channel Characterization by Ray Tracing

“…[21] briefly summarizes and compares some existing LoS probability expressions, but it lacks simulation verification based on real urban environments. Authors in [22] used a GPU-based ray launching algorithm to conduct the verification of the altitude-dependent LoS probability expression derived from [12] in three different cities, but there is a lack of comparison with other LoS probability expressions. To the best of our knowledge, there is a lack of experimental verification for the ITU elevation angle-dependent probability expression of LoS based on the evenly spaced buildings assumption, and the elevation angle-dependent probability expressions of LoS based on the assumption of randomly distributed buildings.…”

Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications

“…The maximum speed-up is obtained using RL approaches, intrinsically more efficient and more suitable than RT to parallelization. GPUbased approaches have been applied with success to propagation prediction in vehicular networks [22], drone-based applications [23,24], and indoor environments represented through point-cloud techniques [25,26].…”