Hyperparameter Optimization of Random Forest Algorithm to Enhance Performance Metric Evaluation of 5G Coverage Prediction
Article Sidebar
Read Counter : 344
Download : 360
Main Article Content
Abstract
Utilizing of 5G technology has become a major focus in the development of more advanced and efficient telecommunications networks. In this context, 5G coverage prediction becomes an important aspect in network planning to ensure optimal user experience. In this study, we explore the use of Random Forest algorithm to predict 5G coverage, with special emphasis on the hyperparameter optimization process to improve model performance. We conduct experiments with various hyperparameter combinations, including 'max_depth', 'max_features', 'min_samples_leaf', 'min_samples_split', and 'n_estimators', using hyperparameter optimization techniques. The results show that by paying attention to the optimal combination of hyperparameters, we managed to significantly improve the performance of the model. The optimized model produces a Minimum Root Mean Squared Error (RMSE) of 0.6, which is much better than the Random Forest model without hyperparameter optimization which has an RMSE of 1.14. The result of this study confirms the importance of the hyperparameter optimization process in improving the accuracy and consistency of the Random Forest model for 5G coverage prediction. The results have important implications in supporting the development of a successful 5G network infrastructure in the future.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Copyright on any article is retained by the author(s).
- Author grant the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
- The article and any associated published material is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
References
Ahmad Fauzi, M. F., Nordin, R., Abdullah, N. F., Alobaidy, H. A. H. H., Fauzi, M. F. A., Nordin, R., Abdullah, N. F., Alobaidy, H. A. H. H., Ahmad Fauzi, M. F., Nordin, R., Abdullah, N. F., & Alobaidy, H. A. H. H. (2022). Mobile Network Coverage Prediction Based on Supervised Machine Learning Algorithms. IEEE Access, 10, 55782–55793. https://doi.org/10.1109/ACCESS.2022.3176619
Barcellos, A. L., Duarte, J. C., & Mendes, A. C. (2023). Radiofrequency Signal Levels Predition Using Machine Learning Models. IEEE Latin America Transactions, 21(2), 351–357. https://doi.org/10.1109/TLA.2023.10015229
Chen, M., Châteauvert, M., & Ethier, J. (2022). Extending Machine Learning Based RF Coverage Predictions to 3D. 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022 - Proceedings, 205–206. https://doi.org/10.1109/AP-S/USNC-URSI47032.2022.9887000
Chiroma, H., Nickolas, P., Faruk, N., Alozie, E., Olayinka, I.-F. Y., Adewole, K., Abdulkarim, A., Oloyede, A., Sowande, O., Garba, S., Usman, A. D., Taura, L. S., & Adediran, Y. (2023). Large Scale Survey for Radio Propagation in Developing Machine Learning Model for Path Losses in Communication Systems. Scientific African, null, null. https://doi.org/10.1016/j.sciaf.2023.e01550
Erunkulu, O. O., Zungeru, A. M., Lebekwe, C., & Chuma, J. (2020). Cellular Communications Coverage Prediction Techniques: A Survey and Comparison. IEEE Access, 8, 113052–113077. https://doi.org/10.1109/ACCESS.2020.3003247
Fauzi, M. F. A., Nordin, R., Abdullah, N. F., Alobaidy, H. A. H., & Behjati, M. (2023). Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization. IEEE Access, 11(November 2022), 3096–3109. https://doi.org/10.1109/ACCESS.2023.3234566
He, R., Gong, Y., Bai, W., Li, Y., & Wang, X. (2020). Random Forests Based Path Loss Prediction in Mobile Communication Systems. 2020 IEEE 6th International Conference on Computer and Communications (ICCC), null, 1246–1250. https://doi.org/10.1109/ICCC51575.2020.9344905
Lee, A. J. and M. S. (2023). Comparative Performance Evaluation of State-of-the-Art Hyperparameter Optimization Frameworks. The Transactions of the Korean Institute of Electrical Engineers, 72(5), 607–620.
Mohammadjafari, S., Roginsky, S., Kavurmacioglu, E., Cevik, M., Ethier, J., & Bener, A. B. (2020). Machine Learning-Based Radio Coverage Prediction in Urban Environments. IEEE Transactions on Network and Service Management, 17(4), 2117–2130. https://doi.org/10.1109/TNSM.2020.3035442
Moraitis, N., Tsipi, L., Vouyioukas, D., Gkioni, A., & Louvros, S. (2021). Performance evaluation of machine learning methods for path loss prediction in rural environment at 3.7 GHz. Wireless Networks, 27, 4169–4188. https://doi.org/10.1007/s11276-021-02682-3
Muniraju, G., Kailkhura, B., Thiagarajan, J. J., Bremer, P. T., Tepedelenlioglu, C., & Spanias, A. (2021). Coverage-Based Designs Improve Sample Mining and Hyperparameter Optimization. IEEE Transactions on Neural Networks and Learning Systems, 32(3), 1241–1253. https://doi.org/10.1109/TNNLS.2020.2982936
Neptune.ai. (n.d.). How to Use Google Colab for Deep Learning – Complete Tutorial. Retrieved September 5, 2023, from https://neptune.ai/blog/how-to-use-google-colab-for-deep-learning-complete-tutorial
Samidi, F. S., Mohamed Radzi, N. A., Mohd Azmi, K. H., Mohd Aripin, N., & Azhar, N. A. (2022). 5G Technology: ML Hyperparameter Tuning Analysis for Subcarrier Spacing Prediction Model. Applied Sciences (Switzerland), 12(16). https://doi.org/10.3390/app12168271
Sotiroudis, S. P., Goudos, S. K., & Siakavara, K. (2019). Neural Networks and Random Forests: A Comparison Regarding Prediction of Propagation Path Loss for NB-IoT Networks. 2019 8th International Conference on Modern Circuits and Systems Technologies, MOCAST 2019, August, 1–4. https://doi.org/10.1109/MOCAST.2019.8741751
Sotiroudis, S. P., Goudos, S. K., & Siakavara, K. (2020). Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity. Telecom, 1(2). https://doi.org/10.3390/telecom1020009
Sousa, M., Alves, A., Vieira, P., Queluz, M., & Rodrigues, A. (2021). Analysis and Optimization of 5G Coverage Predictions Using a Beamforming Antenna Model and Real Drive Test Measurements. IEEE Access, 9, 101787–101808. https://doi.org/10.1109/ACCESS.2021.3097633
Sun, Y., Zhang, J., Zhang, Y., Yu, L., Yuan, Z., Liu, G., & Wang, Q. (2022). Environment Features-Based Model for Path Loss Prediction. IEEE Wireless Communications Letters, 11(9), 2010–2014. https://doi.org/10.1109/LWC.2022.3192516
Wang, C. X., Bian, J., Sun, J., Zhang, W., & Zhang, M. (2018). A survey of 5g channel measurements and models. IEEE Communications Surveys and Tutorials, 20(4), 3142–3168. https://doi.org/10.1109/COMST.2018.2862141
Won, J., Shin, J., Kim, J.-H., & Lee, J.-W. (2023). A Survey on Hyperparameter Optimization in Machine Learning. The Journal of Korean Institute of Communications and Information Sciences, 48(6), 733–747. https://doi.org/10.7840/kics.2023.48.6.733
Yuhana, U. L., Purwarianti, A., & Imamah, I. (2022). Tuning Hyperparameter pada Gradient Boosting untuk Klasifikasi Soal Cerita Otomatis. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 8(1), 134. https://doi.org/10.26418/jp.v8i1.50506
Yuliana, H., Iskandar, & Hendrawan. (2023). A Review of Coverage Prediction in Mobile Communication System using Machine Learning Algorithm. 2023 17th International Conference on Telecommunication Systems, Services, and Applications (TSSA). https://doi.org/10.1109/TSSA59948.2023.10367027
Yuliana, H., Iskandar, & Hendrawan. (2024). Comparative Analysis of Machine Learning Algorithms for 5G Coverage Prediction: Identification of Dominant Feature Parameters and Prediction Accuracy. IEEE Access, 12(January), 18939–18956. https://doi.org/10.1109/ACCESS.2024.3361403