Journal: Volume 31, No. 1, 2026
Pages: 60 – 72
DOI: https://doi.org/10.62660/bcstu/1.2026.60
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Improving the radio signal intensity loss model between the drone and the base station

Roman Zaivyi, Volodymyr Pavlysh
Received 11.08.2025
Revised 28.01.2026
Accepted 16.03.2026
Published 08.04.2026

Abstract

The purpose of the study was to develop a radio signal propagation model that considers the drone’s flight altitude and the specifics of the ground-to-air channel to improve the accuracy of signal loss estimation. The study used comparative analysis of classical models, calculation of signal losses at different altitudes and distances, estimation of the effect of line-of-sight and birefringence propagation, and development and verification of an improved exponential model depending on the height of the drone. It was determined that these models do not account for the substantial influence of the drone’s flight altitude and the increased probability of a line of sight between the drone and the base station, which leads to an overestimation of the predicted signal loss by tens of decibels. Based on the analysis, an improved analytical model was proposed, in which signal losses are defined as the sum of losses in free space and the height-dependent additional term. This additional term is described by an exponential function that decreases with increasing drone altitude and reflects a gradual transition from indirect visibility conditions to line-of-sight conditions. In addition, the model introduced distance correction, taking into account the actual geometry of the ground-toair channel. Calculations performed for the frequencies of 868 MHz and 1,800 MHz showed that the improved model is in good agreement with experimental observations and provides results close to the free space model at significant altitudes. Comparison with the Hata models confirmed that the new model reduces the forecast error in urban conditions to tens of decibels. Considering the flight altitude and gradually moving from indirect to direct visibility greatly increases the accuracy of predicting signal loss for unmanned aerial vehicles. The practical importance of the study lies in the fact that the proposed model enables a more accurate prediction of radio signal losses in the ground-to-air channel, which is the basis for designing reliable communication systems with unmanned aerial vehicles

Keywords

radio communication; altitude; direct visibility; exponential correction; urban environment; analytical model

References

  1. Alavipanah, S.K., et al. (2022). The shadow effect on surface biophysical variables derived from remote sensing: A review. Land, 11(11), article number 2025. doi: 10.3390/land11112025.
  2. Al-Hourani, A., Kandeepan, S., & Jamalipour, A. (2014). Modeling air-to-ground path loss for low altitude platforms. In 2014 IEEE global communications conference (pp. 2898-2904). Austin: IEEE. doi: 10.1109/ GLOCOM.2014.7037248.
  3. Banaseka, F.K., Katsriku, F., Abdulai, J.D., Adu-Manu, K.S., & Engmann, F.N.A. (2021). Signal propagation models in soil medium for the study of wireless underground sensor networks: A review of current trends. Wireless Communications and Mobile Computing, 2021(1), article number 8836426. doi: 10.1155/2021/8836426.
  4. Bennageh, I., Mahmoudi, H., Hajjaji, H., Laabousse, I., & Hamdouchi, A. (2024). Predictive modeling of environmental impact on drone datalink communication system. Modelling and Simulation in Engineering, 2024(1), article number 6151183. doi: 10.1155/2024/6151183.
  5. Chornyi, A., Sidun, G., & Timofeev, P. (2025). Modern methods of Iranian-made unmanned aerial vehicles counteraction. Collection of Scientific Works of the National Academy of the State Border Guard Service of Ukraine. Series: Military and Technical Sciences, 98(1), 92-103. doi: 10.32453/3.v98i1.1802.
  6. De Beelde, B., De Beelde, R., Tanghe, E., Plets, D., Verheyen, K., & Joseph, W. (2022). Vegetation loss at D-band frequencies and new vegetation-dependent exponential decay model. IEEE Transactions on Antennas and Propagation, 70(12), 12092-12103. doi: 10.1109/TAP.2022.3209215.
  7. Djomadji, E.M.D., Kabiena, I.B., Mandengue, J.T., Watching, F., & Tonye, E. (2023). Okumura Hata propagation model optimization in 400 MHz band based on differential evolution algorithm: Application to the city of Bertoua. Journal of Computer and Communications, 11(5), 52-69. doi: 10.4236/jcc.2023.115005.
  8. Elmezughi, M.K., Salih, O., Afullo, T.J., & Duffy, K.J. (2022). Comparative analysis of major machine-learningbased path loss models for enclosed indoor channels. Sensors, 22(13), article number 4967. doi: 10.3390/ s22134967.
  9. ElSayed, M., & Mohamed, M. (2022). The impact of airspace discretization on the energy consumption of autonomous unmanned aerial vehicles (drones). Energies, 15(14), article number 5074. doi: 10.3390/ en15145074.
  10. European Commission: Directorate-General for the Information Society and Media. (1999). COST Action 231 – digital mobile radio towards future generation systems: Final report. Retrieved from https://op.europa.eu/en/ publication-detail/-/publication/f2f42003-4028-4496-af95-beaa38fd475f.
  11. Fuschini, F., Barbiroli, M., Vitucci, E.M., & Degli-Esposti, V. (2022). Multi-band outdoor-to-indoor propagation measurements using a drone. In 2022 16th European conference on antennas and propagation (pp. 1-4). Madrid: IEEE. doi: 10.23919/EuCAP53622.2022.9769075.
  12. Gupta, S. (2013). Comparative path loss analysis of Okumura and COST 231 models for wireless mobile communication using MATLAB simulation. International Journal of Engineering Research & Technology, 2(3).
  13. Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29(3), 317-325. doi: 10.1109/T-VT.1980.23859.
  14. Huang, X., Song, K., Huang, L., Feng, Y., & Wang, Z. (2024). Performance analysis of fuel cells for high altitude long flight multi-rotor drones. SAE Technical Paper, 2024(1), article number 2177. doi: 10.4271/2024-01-2177.
  15. Isabona, J., Emughedi, O., & Roberts, O.O. (2023). Path loss and models: A survey and future perspective for wireless communication networks. International Journal of Advanced Networking and Applications, 15(2), 5892-5907.
  16. Malyi, O.Yu., Pospeieva, I.Ye., Furmanova, N.I., Onyshchenko, V.F., Zaluzhnyi, M.Yu., & Ivanov, V.V. (2024). Methodology for pre-design selection of FPV quadcopter components based on given values of thrust, speed and flight time. Electrical Engineering and Power Engineering, 1, 35-49. doi: 10.15588/1607-6761-2024-1-4.
  17. Marek, D., Paszkuta, M., Szyguła, J., Biernacki, P., Domański, A., Szczygieł, M., Król, M., & Wojciechowski, K. (2024). Swarm of drones in a simulation environment – efficiency and adaptation. Applied Sciences, 14(9), article number 3703. doi: 10.3390/app14093703.
  18. Mirzabeigi, S., Razkenari, R., & Crovella, P. (2025). A review of the potential of drone-based approaches for integrated building envelope assessment. Buildings, 15(13), article number 2230. doi: 10.3390/ buildings15132230.
  19. Moraitis, N., Psychogios, K., & Panagopoulos, A.D. (2023). A survey of path loss prediction and channel models for unmanned aerial systems for system-level simulations. Sensors, 23(10), article number 4775. doi: 10.3390/ s23104775.
  20. Nishad, D.K., Khalid, S., Prakash, D., Singh, V.K., & Sahani, P. (2025). Advanced algorithms for UAV tracking of targets exhibiting start-stop and irregular motion. Scientific Reports, 15, article number 30507. doi: 10.1038/ s41598-025-13698-6.
  21. Oladimeji, T.T., Kumar, P., & Elmezughi, M.K. (2024). An improved path loss model for 5G wireless networks in an enclosed hallway. Wireless Networks, 30, 2353-2364. doi: 10.1007/s11276-024-03675-8.
  22. Ramamoorthy, P., Sanober, S., Di Nunzio, L., & Cardarilli, G.C. (2023). Sustainable power consumption for variance-based integration model in cellular 6G-IoT system. Sustainability, 15(17), article number 12696. doi: 10.3390/su151712696.
  23. Ranchagoda, N.H., Sithamparanathan, K., Ding, M., Al-Hourani, A., & Gomez, K.M. (2021). Elevation-angle based two-ray path loss model for air-to-ground wireless channels. Vehicular Communications, 32, article number 100393. doi: 10.1016/j.vehcom.2021.100393.
  24. Robles-Enciso, R., Juan-Llácer, L., Molina-Garcia-Pardo, J.-M., & Rodríguez, J.-V. (2024). Path loss characterization at the 1800 and 2100 MHz frequency bands in citrus plantations. IEEE Access, 12, 191883-191892. doi: 10.1109/ ACCESS.2024.3519165.
  25. Shawel, B.S., Woldegebreal, D.H., & Pollin, S. (2024). A deep-learning approach to a volumetric radio environment map construction for UAV-assisted networks. International Journal of Antennas and Propagation, 2024(1), article number 9062023. doi: 10.1155/2024/9062023.
  26. Shin, H.-S., Go, S.-H., & Park, J.-H. (2024). Robust radiometric and geometric correction methods for drone-based hyperspectral imaging in agricultural applications. Korean Journal of Remote Sensing, 40(3), 257-268. doi: 10.7780/kjrs.2024.40.3.2.
  27. Tataria, H., Haneda, K., Molisch, A.F., Shafi, M., & Tufvesson, F. (2021). Standardization of propagation models for terrestrial cellular systems: A historical perspective. International Journal of Wireless Information Networks, 28, 20-44. doi: 10.1007/s10776-020-00500-9.
  28. Troyanskiy, O.V., & Kandieiev, P.O. (2025). Classification of unmanned aerial vehicles by types and control technologies. International Science Journal of Engineering & Agriculture, 4(3), 89-100. doi: 10.46299/j. isjea.20250403.08.
  29. Uko, M., Ekpo, S.C., Enahoro, S., & Elias, F. (2024). Performance optimization of 5G-satellite integrated networks for IoT applications in smart cities: A two-ray propagation model approach. Smart Cities, 7(6), 3895-3913. doi: 10.3390/smartcities7060150.
  30. Wang, S., Wang, Y., Bai, X., & Li, D. (2023). Communication efficient, distributed relative state estimation in UAV networks. IEEE Journal on Selected Areas in Communications, 41(4), 1151-1166. doi: 10.1109/JSAC.2023.3242708.
  31. Waqas, M., Abbas, Q., Qureshi, A., Amin, F., de la Torre Díez, I., Rios, C.U., & Gongora, H.F. (2024). Advanced line-of-sight (LOS) model for communicating devices in modern indoor environment. Plos One, 19(7), article number e0305039. doi: 10.1371/journal.pone.0305039.
  32. Xiao, Z., Sun, S., Liu, N., Xu, L., & Wang, L. (2025). Measurements and modeling of air-ground integrated channel in forest environment based on OFDM signals. ArXiv. doi: 10.48550/arXiv.2507.02303.
  33. Zhao, Z., Lin, L., Lu, C., Zhang, R., Liu, K., & Zhang, X. (2025). Earth’s surface reflection and local environment effects. In Radio wave propagation of satellite systems (pp. 97-123). Singapore: Springer. doi: 10.1007/978-98197-1394-3_4.
  34. Zhu, Q., Yao, M., Bai, F., Chen, X., Zhong, W., Hua, B., & Ye, X. (2021). A general altitude-dependent path loss model for UAV-to-ground millimeter-wave communications. Frontiers of Information Technology & Electronic Engineering, 22, 767-776. doi: 10.1631/FITEE.2000497.

Suggested citation

Zaivyi, R., & Pavlysh, V. (2026). Improving the radio signal intensity loss model between the drone and the base station. Bulletin of Cherkasy State Technological University, 31(1), 60-72. https://doi.org/10.62660/bcstu/1.2026.60