Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning
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Date
2024
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Elsevier
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 proposed. 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 subSaharan cities, at mmWaves frequencies. The AHT-SEML model’s performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation 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 pvalues of 1.91 × 10− 14 and 2.50 × 10− 13 for MAE and RMSE, respectively, corroborating significant differences
in the AHT-SEML’s performance