Urban LTE Radio Propagation Modeling Using Genetic Algorithm-Optimized Weighted Ensemble Learning

DOI: https://doi.org/10.33650/jeecom.v8i2.17170
Authors

(1) * Bengawan Alfaresi   (Universitas Muammadiyah Palembang)  
        Indonesia
(2)  Feby Ardianto   (Universitas Muammadiyah Palembang)
(3)  Sofiah Sofiah   (Universitas Muammadiyah Palembang)
(4)  Bungkus Aji Daya   (Universitas Muammadiyah Palembang)
(*) Corresponding Author

Abstract


Accurate path-loss prediction is essential for reliable LTE network planning, particularly in complex urban propagation scenarios where signal attenuation is influenced by radio, geometrical, and environmental factors. This study proposes a Genetic Algorithm (GA)-Based Weighted Blending framework for LTE path-loss prediction using real drive-test measurements collected along the Bus-Way corridor from Jembatan Ampera to Terminal Alang-Alang Lebar in Palembang City, Indonesia, at 1800 MHz and 2100 MHz. The dataset consisted of 19,766 cleaned samples with ten radio, geometrical, and environmental input features. Four heterogeneous machine-learning regressors, namely Random Forest, XGBoost, Multilayer Perceptron (MLP), and Support Vector Regression (SVR), were individually hyperparameter-tuned and combined through GA-based ensemble weight optimization using out-of-fold predictions. The proposed framework was evaluated against empirical propagation models, individual machine-learning models, and average blending using RMSE, MAE, R², and residual diagnostics. The results show that GA-Based Weighted Blending achieved the lowest numerical error among the evaluated models on the testing subset, with an RMSE of 5.5403 dB, MAE of 3.7150 dB, and R² of 0.7967. Compared with average blending, the proposed model reduced RMSE by approximately 2.08%. The optimized weights indicated that XGBoost and MLP provided the largest contribution within the ensemble, while lower-weight learners still contributed complementary prediction characteristics. However, the improvement over XGBoost was relatively small and not statistically significant, indicating that the proposed framework should be interpreted as an adaptive ensemble strategy rather than a definitive replacement for the strongest individual learner.

Keywords

Urban; LTE; Path-loss prediction; Genetic algorithm; Weighted ensemble learning







References


B. Kwon and H. Son, "Accurate Path Loss Prediction Using a Neural Network Ensemble Method," Sensors, vol. 24, no. 1, Art. no. 304, 2024, doi: 10.3390/s24010304.

I. Iliev, Y. Velchev, P. Z. Petkov, B. Bonev, G. Iliev, and I. Nachev, "A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models," Sensors, vol. 24, no. 17, Art. no. 5855, 2024, doi: 10.3390/s24175855.

T. Xu, N. Xu, J. Gao, Y. Zhou, and H. Ma, "Path Loss Prediction Model of 5G Signal Based on Fusing Data and XGBoost-SHAP Method," Sensors, vol. 25, no. 17, Art. no. 5440, 2025, doi: 10.3390/s25175440.

M. Ben Ameur, J. Chebil, M. H. Habaebi, and J. B. H. Tahar, "Machine Learning for Improved Path Loss Prediction in Urban Vehicle-to-Infrastructure Communication Systems," Frontiers in Artificial Intelligence, vol. 8, Art. no. 1597981, 2025, doi: 10.3389/frai.2025.1597981.

C. Nguyen and A. A. Cheema, "A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz," Sensors, vol. 21, no. 15, Art. no. 5100, 2021, doi: 10.3390/s21155100.

N. Sagir and Z. H. Tugcu, "Machine-Learning-Based Path Loss Prediction for Vehicle-to-Vehicle Communication in Highway Environments," Applied Sciences, vol. 14, no. 17, Art. no. 7545, 2024, doi: 10.3390/app14177545.

S. Sung, W. Choi, H. Kim, and J. I. Jung, "Deep Learning-Based Path Loss Prediction for Fifth-Generation New Radio Vehicle Communications," IEEE Access, vol. 11, pp. 75295-75310, 2023, doi: 10.1109/ACCESS.2023.3297215.

T. T. Nguyen, N. Yoza-Mitsuishi, and R. Caromi, "Deep Learning for Path Loss Prediction at 7 GHz in Urban Environment," IEEE Access, vol. 11, pp. 33498-33508, 2023, doi: 10.1109/ACCESS.2023.3264230.

M. Lu, L. Bai, Z. Huang, M. Yang, and X. Cheng, "Path Loss Prediction for Vehicle-to-Infrastructure Communications via Synesthesia of Machines (SoM)," Radio Science, vol. 60, no. 6, pp. 1-15, 2025, doi: 10.1029/2024RS008187.

S. Duangsuwan, P. Juengkittikul, and M. 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, Art. no. 5524709, 2021, doi: 10.1155/2021/5524709.

T. Mahjoub, A. Ben Mnaouer, M. Ben Said, and H. Boujemaa, "LoRa Signal Propagation and Path Loss Prediction in Tunisian Date Palm Oases," Computers and Electronics in Agriculture, vol. 222, Art. no. 109027, 2024, doi: 10.1016/j.compag.2024.109027.

R. R. Guerra, A. Vizziello, P. Savazzi, E. Goldoni, and P. Gamba, "Forecasting LoRaWAN RSSI Using Weather Parameters: A Comparative Study of ARIMA, Artificial Intelligence and Hybrid Approaches," Computer Networks, vol. 243, Art. no. 110258, 2024, doi: 10.1016/j.comnet.2024.110258.

M. Moreno-Cuevas, J. Lorente-Lopez, J.-V. Rodriguez, C. Sanchis-Borras, J. M. Molina-Garcia-Pardo, and J. Aznar-Poveda, "Geospatial Feature-Based Path Loss Prediction at 1800 MHz in Covenant University Campus with Tree Ensembles, Kernel-Based Methods, and a Shallow Neural Network," Electronics, vol. 14, no. 20, Art. no. 4112, 2025, doi: 10.3390/electronics14204112.

H. Fhima, H. Zormati, J. Chebil, M. H. Habaebi, and J. B. H. Tahar, "A Two-Stage Machine Learning Approach to Path Loss Prediction at 5.9 GHz: Benchmarking Empirical and Data-Driven Models for V2V Highway Scenarios," Annals of Telecommunications, 2026, doi: 10.1007/s12243-026-01178-5.

M. O. Ojo, I. Viola, S. Miretti, E. Martignani, S. Giordano, and M. Baratta, "A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring," Sensors, vol. 24, no. 10, Art. no. 2991, 2024, doi: 10.3390/s24102991.

N. Faruk, Q. R. Adebowale, I.-F. Y. Olayinka, K. S. Adewole, A. Abdulkarim, A. A. Oloyede, H. Chiroma, O. A. Sowande, L. A. Olawoyin, S. Garba, A. D. Usman, Y. A. Adediran, and L. S. Taura, "ANN-Based Model for Multiband Path Loss Prediction in Built-Up Environments," Scientific African, vol. 17, Art. no. e01350, 2022, doi: 10.1016/j.sciaf.2022.e01350.

M. K. Elmezughi, T. J. Afullo, N. O. Oyie, O. Salih, and K. J. Duffy, "Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels," Sensors, vol. 22, no. 13, Art. no. 4967, 2022, doi: 10.3390/s22134967.

M. K. Elmezughi, O. Salih, T. J. Afullo, and K. J. Duffy, "Path Loss Modeling Based on Neural Networks and Ensemble Method for Future Wireless Networks," Heliyon, vol. 9, no. 9, Art. no. e19685, 2023, doi: 10.1016/j.heliyon.2023.e19685.

O. J. Famoriji and T. Shongwe, "Path Loss Prediction in Tropical Regions Using Machine Learning Techniques: A Case Study," Electronics, vol. 11, no. 17, Art. no. 2711, 2022, doi: 10.3390/electronics11172711.

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, doi: 10.1109/ACCESS.2022.3217912.

M. Alnatoor, M. Omari, and M. Kaddi, "Path Loss Models for Cellular Mobile Networks Using Artificial Intelligence Technologies in Different Environments," Applied Sciences, vol. 12, no. 24, Art. no. 12757, 2022, doi: 10.3390/app122412757.

A. Gupta, J. Du, D. Chizhik, R. A. Valenzuela, and M. Sellathurai, "Machine Learning-Based Urban Canyon Path Loss Prediction Using 28 GHz Manhattan Measurements," IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4096-4111, 2022, doi: 10.1109/TAP.2022.3152776.

S. P. Sotiroudis, A. D. Boursianis, S. K. Goudos, and K. Siakavara, "From Spatial Urban Site Data to Path Loss Prediction: An Ensemble Learning Approach," IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 5526-5538, 2022, doi: 10.1109/TAP.2021.3138257.

S. P. Sotiroudis, P. Sarigiannidis, S. K. Goudos, and K. Siakavara, "Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach," IEEE Access, vol. 9, pp. 30441-30451, 2021, doi: 10.1109/ACCESS.2021.3059589.

I. F. M. Rafie, S. Y. Lim, and M. J. H. Chung, "Path Loss Prediction in Urban Areas: A Machine Learning Approach," IEEE Antennas and Wireless Propagation Letters, vol. 22, no. 4, pp. 809-813, 2023, doi: 10.1109/LAWP.2022.3225792.

J. Lorente-Lopez, I. Rodriguez-Rodriguez, J.-V. Rodriguez, J. M. Molina-Garcia-Pardo, and J. Aznar-Poveda, "Machine-Learning-Based Urban Path Loss Prediction at 900 MHz: Principal Component Analysis, Clustering, Feature Importance and Regression," IEEE Open Journal of Antennas and Propagation, vol. 7, no. 1, pp. 40-56, 2026, doi: 10.1109/OJAP.2025.3629578.

S. Ojo, M. Akkaya, and J. C. Sopuru, "An Ensemble Machine Learning Approach for Enhanced Path Loss Predictions for 4G LTE Wireless Networks," International Journal of Communication Systems, vol. 35, no. 7, Art. no. e5101, 2022, doi: 10.1002/dac.5101.

A. Marey, M. Bal, H. F. Ates, and B. K. Gunturk, "PL-GAN: Path Loss Prediction Using Generative Adversarial Networks," IEEE Access, vol. 10, pp. 90474-90480, 2022, doi: 10.1109/ACCESS.2022.3201643.

J.-H. Lee and A. F. Molisch, "A Scalable and Generalizable Pathloss Map Prediction," IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 17793-17806, 2024, doi: 10.1109/TWC.2024.3457431.

G. Chen, Y. Liu, T. Zhang, J. Zhang, X. Guo, and J. Yang, "A Graph Neural Network Based Radio Map Construction Method for Urban Environment," IEEE Communications Letters, vol. 27, no. 5, pp. 1327-1331, 2023, doi: 10.1109/LCOMM.2023.3260272.

R. Levie, C. Yapar, G. Kutyniok, and G. Caire, "RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks," IEEE Transactions on Wireless Communications, vol. 20, no. 6, pp. 4001-4015, 2021, doi: 10.1109/TWC.2021.3054977.

A. Seretis and C. D. Sarris, "Toward Physics-Based Generalizable Convolutional Neural Network Models for Indoor Propagation," IEEE Transactions on Antennas and Propagation, vol. 70, no. 6, pp. 4112-4126, 2022, doi: 10.1109/TAP.2021.3138535.

H. Cheng, S. Ma, H. Lee, and M. Cho, "Millimeter Wave Path Loss Modeling for 5G Communications Using Deep Learning With Dilated Convolution and Attention," IEEE Access, vol. 9, pp. 62867-62879, 2021, doi: 10.1109/ACCESS.2021.3070711.

J. O. Afape, A. A. Willoughby, M. E. Sanyaolu, O. O. Obiyemi, K. Moloi, J. O. Jooda, and O. F. Dairo, "Improving Millimetre-Wave Path Loss Estimation Using Automated Hyperparameter-Tuned Stacking Ensemble Regression Machine Learning," Results in Engineering, vol. 22, Art. no. 102289, 2024, doi: 10.1016/j.rineng.2024.102289.

T. T. Oladimeji, P. Kumar, and N. O. Oyie, "Propagation Path Loss Prediction Modelling in Enclosed Environments for 5G Networks: A Review," Heliyon, vol. 8, no. 11, Art. no. e11581, 2022, doi: 10.1016/j.heliyon.2022.e11581.

K. Ahmad and S. Hussain, "Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels," IEEE Access, vol. 10, pp. 113690-113698, 2022, doi: 10.1109/ACCESS.2022.3218622.

S. Sukemi, A. F. Oklilas, M. W. Fadli, and B. Alfaresi, "Path Loss Prediction Accuracy Based on Random Forest Algorithm in Palembang City Area," Jurnal Nasional Teknik Elektro, vol. 12, no. 1, 2023, doi: 10.25077/jnte.v12n1.1051.2023.


Dimensions, PlumX, and Google Scholar Metrics

10.33650/jeecom.v8i2.17170


Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Bengawan Alfaresi, feby ardianto, Sofiah Sofiah

 
This work is licensed under a Creative Commons Attribution License (CC BY-SA 4.0)

Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.