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Measurement of Path Loss Characterization and Prediction Modeling for Swarm UAVs Air-to-Air Wireless Communication Systems

Sarun Duangsuwan
Electrical Engineering, Department of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Prince of Chumphon Campus, 17/1 Chumcoo District, Pathio, Chumphon, Thailand

Abstract—A challenge swarm unmanned aerial vehicles (swarm UAVs)-based wireless communication systems have been focused on channel modeling in various environments. In this paper, we present the characterized path loss air-to-air (A2A) channel modeling-based measurement and prediction model. The channel model was considered using A2A Two-Ray (A2AT-R) extended path loss modeling. The prediction model was considered using an artificial neural network (ANN) algorithm to train the measured dataset. To evaluate the measurement result, path loss models between the A2AT-R model and the prediction model are shown. We show that the prediction model using ANN is optimal to train the measured data for the A2A channel model. To discuss the result, the parametric prediction errors such as mean absolute error (MAE), root mean square error (RMSE), and R-square (R2), are performed.
 
Index Terms—Path loss characterization, prediction model, air-to-air wireless communication system, ANN algorithm, swarm UAVs

Cite: Sarun Duangsuwan, "Measurement of Path Loss Characterization and Prediction Modeling for Swarm UAVs Air-to-Air Wireless Communication Systems," Journal of Communications vol. 16, no. 6, pp. 228-235, June 2021. Doi: 10.12720/jcm.16.6.228-235

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.