Validation of ITU-R P.833-9 tree attenuation model for Land Mobile Satellite propagation channel at Ku/Ka band

Deploying 5G networks in mountainous rural regions can be challenging due to its unique and challenging characteristics. Attaching a transmitter to a UAV to enable connectivity requires a selection of suitable propagation models in such conditions. This research paper comprehensively investigates the signal propagation and performance under multiple frequencies, from mid-band to mmWaves range (3.5, 6, 28, and 60 GHz). The study focuses on rural mountainous regions, which were empirically simulated based on the Skardu, Pakistan, region. A complex 3D ray tracing method carefully figures out the propagation paths using the geometry of a 3D environment and looks at the effects in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The analysis considers critical parameters such as path loss, received power, weather loss, foliage loss, and the impact of varying UAV heights. Based on the analysis and regression modeling techniques, quadratic polynomials were found to accurately model the signal behavior, enabling signal strength predictions as a function of distances between the user and an elevated drone. Results were analyzed and compared with suburban areas with no mountains but more compact buildings surrounding the Universiti Kebangsaan Malaysia (UKM) campus. The findings highlight the need to identify the optimal height for the UAV as a base station, characterize radio channels accurately, and predict coverage to optimize network design and deployment with UAVs as additional sources. The research offers valuable insights for optimizing signal transmission and network planning and resolving spectrum-management difficulties in mountainous areas to enhance wireless communication system performance. The study emphasizes the significance of visualizations, statistical analysis, and outlier detection for understanding signal behavior in diverse environments.

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Validation of ITU-R P.833-9 tree attenuation model for Land Mobile Satellite propagation channel at Ku/Ka band

Cited by 3 publications

References 8 publications

Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure

Machine Learning Regression for RF Path Loss Estimation Over Grass Vegetation in IoWSN Monitoring Infrastructure

Propagation Path Loss Models in Forest Scenario at 605 MHz

Propagation Modeling of Unmanned Aerial Vehicle (UAV) 5G Wireless Networks in Rural Mountainous Regions Using Ray Tracing

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