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 relay-stations 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.

A Multi-Frequency Investigation of Air-To-Ground Urban Propagation Using a GPU-based Ray Launching Algorithm / Arpaio M.J.; Vitucci E.M.; Barbiroli M.; Degli-Esposti V.; Masotti D.; Fuschini F.. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 9:(2021), pp. 9393891.54407-9393891.54419. [10.1109/ACCESS.2021.3070832]

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

Arpaio M. J.
;
Vitucci E. M.;Barbiroli M.;Degli-Esposti V.;Masotti D.;Fuschini F.
2021

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 relay-stations 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.
2021
A Multi-Frequency Investigation of Air-To-Ground Urban Propagation Using a GPU-based Ray Launching Algorithm / Arpaio M.J.; Vitucci E.M.; Barbiroli M.; Degli-Esposti V.; Masotti D.; Fuschini F.. - In: IEEE ACCESS. - ISSN 2169-3536. - ELETTRONICO. - 9:(2021), pp. 9393891.54407-9393891.54419. [10.1109/ACCESS.2021.3070832]
Arpaio M.J.; Vitucci E.M.; Barbiroli M.; Degli-Esposti V.; Masotti D.; Fuschini F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/818666
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