The Effect of Rainfall on the UAV Placement for 5G Spectrum in Malaysia

In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user�s antenna height, rain attenuation, and the wall penetration loss at hi...

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Bibliographic Details
Main Authors: Shalaby A.M., Othman N.S.
Other Authors: 57219433216
Format: Article
Published: MDPI 2023
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Summary:In this paper, the influence of rainfall on the deployment of UAV as an aerial base station in the Malaysia 5G network is studied. The outdoor-to-outdoor and outdoor-to-indoor path loss models are derived by considering the user�s antenna height, rain attenuation, and the wall penetration loss at high frequencies. The problem of finding the UAV 3D placement is formulated with the objective to minimize the total path loss between the UAV and all users. The problem is solved by invoking two algorithms, namely Particle Swarm Optimization (PSO) and Gradient Descent (GD) algorithms. The performance of the proposed algorithms is evaluated by considering two scenarios to determine the optimum location of the UAV, namely outdoor-to-outdoor and outdoor-to-indoor scenarios. The simulation results show that, for the outdoor-to-outdoor scenario, both algorithms resulted in similar UAV 3D placement unlike for the outdoor-to-indoor scenario. Additionally, in both scenarios, the proposed algorithm that invokes PSO requires less iterations to converge to the minimum transmit power compared to that of the algorithm that invokes GD. Moreover, it is also observed that the rain attenuation increases the total path loss for high operating frequencies, namely at 24.9 GHz and 28.1 GHz. Hence, this resulted in an increase of UAV required transmit power. At 28.1 GHz, the presence of rain at the rate of 250 mm/h resulted in an increase of UAV required transmit power by a factor of 4 and 15 for outdoor-to-outdoor and outdoor-to-indoor scenarios, respectively. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.