Analysis and prediction of path loss in UAVBS air-to-ground communication using neural networks
Resumo
Unmanned aerial vehicles bases stations (UAVBS) have many applications in telecommunications. Enables integration into systems in order to provide network signals for users on the ground. The electromagnetic signal from the UAV is characterized by air-to-ground propagation. At different altitudes, the signal suffers losses along the way, thus facing several problems related to transmissions, such as attenuation, fading, and distortion. This paper studies UAV air-to-ground path loss at different altitudes of the UAV. To this, implement a field measurement campaign, which collects and analyzes the signal strength in wireless networks. Finally, it proposes the use of recurrent neural networks to predict the propagation loss in the network. The results were found to show good accuracy in the chosen scenario.
Referências
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AlAmmouri, Ahmad Andrews, Jeffrey Baccelli, Francois. (2017). SINR and Throughput of Dense Cellular Networks With Stretched Exponential Path Loss. IEEE Transactions on Wireless Communications. PP. 10.1109/TWC.2017.2776905.
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M. K. Joyo, D. Hazry, S. Faiz Ahmed, M. H. Tanveer, F. A. Warsi and A. T. Hussain, Altitude and horizontal motion control of quadrotor UAV in the presence of air turbulence," 2013 IEEE Conference on Systems, Process Control (ICSPC), Kuala Lumpur, 2013, pp. 16-20, doi: 10.1109/SPC.2013.6735095.
SARFO K., WANG K., Sarpong k., "Large-scale UAV-Network using the Hata Okumura model with PSO algorithm for Open Area Communication," 2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI), Shanghai, China, 2021, pp. 138-142, doi: 10.1109/ICCEAI52939.2021.00026.
KHATUN, Eliza. COMPARATIVE ANALYSIS OF OKUMURA-HATA AND COST 231 PATH LOSS MODELS FOR WIRELESS MOBILE COMMUNICATIONS. Optimization of LTE 1800 MHz Network Propagation Loss Models using Genetic Algorithm. XXXIV BRAZILIAN TELECOMMUNICATION SYMPOSIUM. 2016
SHOER I., GUNTURK KB, ATES, HF Altitude Optimization of UAV Base Stations from Satellite Images Using Recurrent Neural Network. arXiv:2112.14551v1 [cs.LG] December 29, 2021
SARUN D. and MYO MM, "Comparison of Path Loss Prediction Models for UAV and IoT Air-to-Ground Communication System in Rural Precision Farming Environment," Journal of Communications vol. 16, no. 2, pp. 60-66, February 2021. Doi: 10.12720/jcm.16.2.60-66
Y. Shi, R. Enami, J. Wensowitch and J. Camp, "Measurement-based characterization of LOS and NLOS drone-to-ground channels," 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain, 2018, pp. 1-6, doi: 10.1109/WCNC.2018.8377104.
Yan Zhang, Jinxiao Wen, Guanshu Yang, Zunwen He, Xinran Luo, "Air-to-Air Path Loss Prediction Based on Machine Learning Methods in Urban Environments", Wireless Communications and Mobile Computing, vol. 2018, Article ID 8489326, 9 pages, 2018. https://doi.org/10.1155/2018/8489326
S. I. Popoola et al., "Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel," in IEEE Access, vol. 7, pp. 150462-150483, 2019, doi: 10.1109/ACCESS.2019.2947009.
L. Wu et al., "Artificial Neural Network Based Path Loss Prediction for Wireless Communication Network," in IEEE Access, vol. 8, pp. 199523-199538, 2020, doi: 10.1109/ACCESS.2020.3035209.
S. K. Goudos, G. V. Tsoulos, G. Athanasiadou, M. C. Batistatos, D. Zarbouti and K. E. Psannis, "Artificial Neural Network Optimal Modeling and Optimization of UAV Measurements for Mobile Communications Using the L-SHADE Algorithm," in IEEE Transactions on Antennas and Propagation, vol. 67, no. 6, pp. 4022-4031, June 2019, doi: 10.1109/TAP.2019.2905665.
Sarun Duangsuwan, Phakamon Juengkittikul, Myo Myint Maw, "Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios", International Journal of Antennas and Propagation, vol. 2021, Article ID 5524709, 13 pages, 2021. https://doi.org/10.1155/2021/5524709
AlAmmouri, Ahmad Andrews, Jeffrey Baccelli, Francois. (2017). SINR and Throughput of Dense Cellular Networks With Stretched Exponential Path Loss. IEEE Transactions on Wireless Communications. PP. 10.1109/TWC.2017.2776905.
Azdy, Rezania Darnis, Febriyanti. (2020). Use of Haversine Formula in Finding Distance Between Temporary Shelter and Waste End Processing Sites. Journal of Physics: Conference Series. 1500. 012104. 10.1088/1742-6596/1500/1/012104.
W. Giernacki, J. Rao, S. Sladic, A. Bondyra, M. Retinger and T. Espinoza-Fraire, "DJI Tello Quadrotor as a Platform for Research and Education in Mobile Robotics and Control Engineering," 2022 International Conference on Unmanned Aircraft Systems (ICUAS), Dubrovnik, Croatia, 2022, pp. 735-744, doi: 10.1109/ICUAS54217.2022.9836168.
M. K. Joyo, D. Hazry, S. Faiz Ahmed, M. H. Tanveer, F. A. Warsi and A. T. Hussain, Altitude and horizontal motion control of quadrotor UAV in the presence of air turbulence," 2013 IEEE Conference on Systems, Process Control (ICSPC), Kuala Lumpur, 2013, pp. 16-20, doi: 10.1109/SPC.2013.6735095.
Publicado
22/05/2023
Como Citar
SILVA, Wilson R. S.; TORRES, Renato H.; CARDOSO, Diego L..
Analysis and prediction of path loss in UAVBS air-to-ground communication using neural networks. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 7. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 107-120.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2023.814.
