Evaluation of the Effect of Model Deviation in Air-to-Ground Signal Propagation on Cell Area of Coverage
DOI:
https://doi.org/10.3991/ijoe.v22i01.58971Keywords:
UAV, base station, drone-cell, propagation model, air-to-ground, Gaussian, empirical CDFAbstract
With the broadening capacity of the Internet-of-Things (IoT) framework, unmanned aerial vehicles (UAVs) are predicted to contribute to the enhancement of the variety of means of offered IoT services if they are equipped with wireless connectivity modules (e.g., LTE, 5G/6G cellular, LPWAN, WiFi, and satellite) for information exchange. Within this IoT scheme that employs wireless cellular networking, an UAV could constitute an aerial base station to act as the intermediate layer of the communication scheme and architecture. High-fidelity communications are necessary to ensure the superior functionality of this architecture. Therefore, accurate propagation modeling of the air-to-ground (A-to-G) UAV transmissions in an aerial base station cell is a prerequisite for designing and conducting an investigation of the performance of UAVs in various network topologies. We investigate the resulting deviations of a generic A-to-G path-loss (PL) propagation model from that of a custom-constructed (Empirical) A-to-G PL and shadowing model, which is based on environment and field measurements in conjunction with a specific frequency channel and network topology. To this end, analysis of performed computer simulations and numerical calculations corroborates our findings. Our conducted research for this dense urban environment setting reveals that a Gaussian (Normal) probability distribution function, when compared with our Empirical one, will overestimate the likelihood of received signal power to be equal to or larger than a desired threshold level by up to 9% in the vicinity of the border of an UAV cell, in addition to the associated percentage coverage area in both of the considered cell topologies. This could provide for a cost-effective, scalable, and accurate algorithm implementation for wireless operators’ coverage estimation and planning.
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Copyright (c) 2025 Ashraf A. Tahat, Talal A. Edwan
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