The paper discussed in this largely explored the effectiveness of machine-learning-based models in wireless link path loss predictions, in lieu of traditional models like Longley-Rice and free space path loss (FSPL). Traditional models suffer in accuracy in non-line-of-sight scenarios due to their inability to account for signal attenuation, or interference caused by electromagnetic interplay with terrain and clutter.
The paper provides a solution by proposing simple features derived from path profiles as the primary input for a predictor of path loss along a wireless link. This revolutionary approach to path loss modeling uses far fewer features than models relying on sophisticated high-resolution imagery and detailed path profiles.
To ascertain the accuracy of the proposed approach, researchers compared machine-learning-based modeling with traditional methods and underscored the necessity of using measurement data for training to ensure the best results. The study used the openly available ITU-R UK Ofcom drive test dataset. DTM and DSM databases were also used to extract path profiles and derive features like terrain depth, total obstacle depth along the direct path, and clutter depth.
Three different feature configurations were used in the study and processed by three different modeling techniques: curve-fit log regression, fully-connected neural networks (FCNs), and boosted trees (XGBoost).
The results highlighted the superiority of the FCN model over boosted trees and log regression. The introduction of the third feature–obstacle depth–improved the model’s performance while separating terrain and clutter depths resulted in insignificant improvements.
That said, the FCN model learned behaviours grounded in physics, implying increased obstacle loss as predicted with increases in frequency and obstacle depth. Nonetheless, certain limitations were noted, like the impact of link distance on obstacle loss and non-zero obstacle loss even at zero obstacle depth. These challenges are a focus for future work and further improvement.
Overall, these findings are foreseen to have wide-ranging implications for enhancing wireless network planning, deployment, and optimization. As such, the study paves the way for more precise and efficient propagation modeling in the wireless communication landscape. The use of simplified features and machine learning in path loss model training is recognized as a breakthrough in this field, setting the stage for potential future advancements.
