Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning
| dc.contributor.author | Janet O. Jooda | |
| dc.date.accessioned | 2025-05-23T12:50:40Z | |
| dc.date.available | 2025-05-23T12:50:40Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was pro posed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model’s performance was optimised using the AHT tuning technique. The AHT-SEML model’s efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub- Saharan cities, at mmWaves frequencies. The AHT-SEML model’s performance was compared to three empir ical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using eval uation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML’s t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p- values of 1.91 × 10− 14 and 2.50 × 10− 13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML’s performance. | |
| dc.identifier.citation | Improving Millimetre-Wave Path Loss Estimation Using Automated Hyperparameter-Tuned Stacking Ensemble Regression Machine Learning, Results in Engineering, Vol. 22, Pp. 1 – 18 | |
| dc.identifier.uri | 10.1016/j.rineng.2024.102289 | |
| dc.identifier.uri | https://repository.run.edu.ng/handle/123456789/4636 | |
| dc.language.iso | en | |
| dc.publisher | Results in Engineering | |
| dc.relation.ispartofseries | Vol. 22 | |
| dc.title | Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning | |
| dc.type | Article |