Air-to-Ground Path Loss Modeling in UAV Networks Via GSA-Based Hyperparameter Optimization

Authors

  • Pham Thi Quynh Trang Department of Electronic Engineering Hanoi University of Industry, Hanoi, 100000, Vietnam, Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam
  • Nguyen Thi Phuoc Van Department of Information Technology Thanh Do University, Hanoi, 100000, Vietnam
  • Duong Thi Hang Department of Electronic and Telecommunication Engineering Electric Power, Hanoi, 100000, Vietnam
  • Dinh Trieu Duong Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam
  • Trinh Anh Vu Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam

DOI:

https://doi.org/10.13052/2026.ACES.J.410402

Keywords:

Air-to-Ground channel modeling, Bayesian search, deep learning, gravitational search algorithm, hyperparameter optimization, machine learning, path-loss prediction, random search, unmanned aerial vehicles

Abstract

In Unmanned Aerial Vehicle (UAV) communications, Air-to-Ground (A2G) channel modeling is complex due to high mobility and environmental dynamics. While Machine Learning (ML) and Deep Learning (DL) techniques have been adopted to improve prediction accuracy over traditional empirical models, their performance remains highly dependent on hyperparameter configuration. Recent techniques such as Random Search and Bayesian Search are commonly used for hyperparameter tuning; however, they often struggle with convergence efficiency and prediction stability. To address these challenges, this study aims to develop a hyperparameter tuning framework based on the Gravitational Search Algorithm (GSA) to enhance the predictive performance of ML-based A2G models. The framework is applied to K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), and Long Short-Term Memory (LSTM) models at 1 GHz, 2 GHz, and 5.8 GHz. Experimental results demonstrate that GSA-optimized models demonstrate improved predictive stability and competitive accuracy, with GSA-LSTM and GSA-RF achieving an Root Mean Square Error (RMSE) of 5.46 dB, representing a 56% improvement over the free-space model. The proposed approach demonstrates improved robustness compared to conventional search strategies.

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Author Biographies

Pham Thi Quynh Trang, Department of Electronic Engineering Hanoi University of Industry, Hanoi, 100000, Vietnam, Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam

Pham Thi Quynh Trang received the B.S. and M.S. degrees from VNU University of Engineering and Technology in 2000 and 2006, respectively. She is currently pursuing a Ph.D. degree in telecommunication engineering at VNU. She is interested in wireless communication, wireless sensor network optimization algorithms, digital signal processing, neural networks, applications of nature-inspired algorithms, and FPGA technology.

Nguyen Thi Phuoc Van, Department of Information Technology Thanh Do University, Hanoi, 100000, Vietnam

Nguyen Thi Phuoc Van completed her doctoral degree at Massey University in New Zealand in 2020, specializing in the School of Engineering and Advanced Technology. From 2020 to 2022, she worked as a researcher at the Big Data Integration Research Center at the National Institute of Information and Communications Technology (NICT) in Japan. She was a research fellow at the Centre for Health Research, University of Southern Queensland, Australia, from 2022 to 2024. Currently, she is a lecturer at Thanh Do University in Hanoi, Vietnam. Her research interests encompass communication systems, vital signs sensing systems, sensing technology for monitoring human healthcare conditions, and the application of artificial intelligence in communication and healthcare navigator systems.

Duong Thi Hang, Department of Electronic and Telecommunication Engineering Electric Power, Hanoi, 100000, Vietnam

Duong Thi Hang has been a lecturer at the Faculty of Electronic Engineering, Hanoi University of Industry since 2000. She received the B.S and M.S. degrees from VNU University of Engineering and Technology in 2000 and 2005, respectively. She is currently pursuing a Ph.D. degree in telecommunication engineering at VNU. Her main research interests are indoor positioning systems, machine learning, pattern classification, and nature-inspired algorithm applications.

Dinh Trieu Duong, Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam

Dinh Trieu Duong is a lecturer at Wireless Communication Department of VNU University of Engineering and Technology. His main research interests are Signal Processing for Multimedia Communications, Development of High Performance Multimedia Image, Video Codecs for Real-time Image, Video Transmission over Wire/Wireless Networks.

Trinh Anh Vu, Department of Electronic and Telecommunication Engineering Vietnam National University, Hanoi, 100000, Vietnam

Trinh Anh Vu is a retired Assoc. Prof. at Wireless Communication Department of VNU University of Engineering and Technology. His main research interests are High-speed Transmission in wireless communications, Massive MIMO systems for 5G, Milimeter wave communications, FPGA design of communication System.

References

M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. Jang, “6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions,” IEEE Open Journal of the Communications Society, vol. 1, pp. 957–975, 2020.

O. T. H. Alzubaidi, M. N. Hindia, K. Dimyati, K. A. Noordin, A. N. A. Wahab, F. Qamar, and R. Hassan, “Interference challenges and management in B5G network design: A comprehensive review,” Electronics, vol. 11, 2022.

B. Li, Z. Fei, and Y. Zhang, “UAV communications for 5G and beyond: Recent advances and future trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, 2019.

M. Polese, M. Giordani, T. Zugno, A. Roy, S. Goyal, D. Castor, and M. Zorzi, “Integrated access and backhaul in 5G mmWave networks: Potential and challenges,” IEEE Communications Magazine, vol. 58, no. 3, pp. 62–68, 2020.

T. S. Rappaport, Y. Xing, G. R. MacCartney, A. F. Molisch, E. Mellios, and J. Zhang, “Overview of millimeter wave communications for fifth-generation (5G) wireless networks—with a focus on propagation models,” IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6213–6230, 2017.

Q. Zhu, M. Yao, F. Bai, X. Chen, W. Zhong, B. Hua, and X. Ye, “A general altitude-dependent path loss model for UAV-to-ground millimeter-wave communications,” Front Inform Technol Electron Eng 22, vol. 22, p. 767–776, 2021.

N. E.-D. Safwat, F. Newagy, and I. M. Hafez, “Air-to-ground channel model for UAVs in dense urban environments,” IET Communications, vol. 14, pp. 1751–8628, 2020.

K. J. Jang, S. Park, J. Kim, Y. Yoon, C.-S. Kim, Y.-J. Chong, and G. Hwang, “Path loss model based on machine learning using multi-dimensional Gaussian process regression,” IEEE Access, vol. 10, pp. 115061–115073, 2022.

A. Tahat, T. Edwan, H. Al-Sawwaf, J. Al-Baw, and M. Amayreh, “Simplistic machine learning-based air-to-ground path loss modeling in an urban environment,” in 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 177–183, IEEE, Paris, France, 2020.

J. Ethier and M. Chateauvert, “Machine learning-based path loss modeling with simplified features,” IEEE Antennas and Wireless Propagation Letters, vol. 23, no. 7, pp. 2238–2242, 2024.

B. Bischl and M. Binder, “Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 13, no. 2, p. e1484, 2023.

N. E.-D. Safwat, “Path loss data for UAV channel modeling,” Mendeley Data, 2021.

T. S. Rappaport, Y. Xing, G. R. MacCartney, A. F. Molisch, E. Mellios, and J. Zhang, “Overview of millimeter wave communications for fifth-generation (5G) wireless networks—with a focus on propagation models,” IEEE Transactions on Antennas and Propagation, vol. 65, no. 12, pp. 6213–6230, 2017.

E. Elgeldawi, A. Sayed, A. R. Galal, and A. M. Zaki, “Hyperparameter tuning for machine learning algorithms used for Arabic sentiment analysis,” Informatics, vol. 8, no. 4, p. 79, 2021.

P. Probst, A.-L. Boulesteix, and B. Bischl, “Tunability: Importance of hyperparameters of machine learning algorithms,” Journal of Machine Learning Research, vol. 20, no. 53, pp. 1–32, 2019.

P. R. Lorenzo, J. Nalepa, M. Kawulok, L. S. Ramos, and J. Ranilla, “Particle swarm optimization for hyper-parameter selection in deep neural networks,” in Proceedings of the Genetic and Evolutionary Computation Conference, GECCO ’17, pp. 481–488, 2017.

J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, no. 10, pp. 281–305, 2012.

V. Nguyen, “Bayesian optimization for accelerating hyper-parameter tuning,” in 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 302–305, 2019.

F. G. Lobo, D. E. Goldberg, and M. Pelikán, “Time complexity of genetic algorithms on exponentially scaled problems,” in Annual Conference on Genetic and Evolutionary Computation, pp. 151–158, Morgan Kaufmann Publishers, Las Vegas, Nevada, USA, 2000.

F. Itano, M. A. de Abreu de Sousa, and E. Del-Moral-Hernandez, “Extending MLP ANN hyper-parameters optimization by using genetic algorithm,” in 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–5, IEEE, Rio de Janeiro, Brazil, 2018.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020.

A. Hashemi, M. B. Dowlatshahi, and H. Nezamabadi-Pour, “Gravitational search algo rithm: Theory, literature review, and applications,” Handbook of AI-based Metaheuristics, pp. 119–150, 2021.

D. Ezzat, A. E. Hassanien, and H. A. Ella, “An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization,” Applied Soft Computing, vol. 98, pp. 106742–106742, 2020.

W. M. Alenazy and A. S. Alqahtani, “Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition,” J Ambient Intell Human Comput, vol. 13, no. 2, pp. 829–844, 2020.

J. Isabona and V. M. Srivastava, “Hybrid neural network approach for predicting signal propagation loss in urban microcells,” in 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), pp. 1–6, IEEE, Agra, India, 2016.

scikit learn. (2025). User Guide [Online]. Available: https://scikitlearn.org/stable/user_guide.html

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Published

2026-04-30

How to Cite

[1]
P. T. Q. . Trang, N. T. P. . Van, D. T. . Hang, D. T. . Duong, and T. A. . Vu, “Air-to-Ground Path Loss Modeling in UAV Networks Via GSA-Based Hyperparameter Optimization”, ACES Journal, vol. 41, no. 04, pp. 306–314, Apr. 2026.

Issue

2026: Vol 41 Iss 4

Section

General Submission

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