Geometrical Features Based-mmWave UAV Path Loss Prediction Using Machine Learning for 5G and Beyond

Unmanned aerial vehicles (UAVs) are envisioned to play a pivotal role in modern telecommunication and wireless sensor networks, offering unparalleled flexibility and mobility for communication and data collection in diverse environments. This paper presents a comprehensive investigation into the performance of supervised machine learning (ML) models for path loss (PL) prediction in UAV-assisted millimeter-wave (mmWave) radio networks. Leveraging a unique set of interpretable geometrical features, six distinct ML models–linear regression (LR), support vector regressor (SVR), K nearest neighbors (KNN), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN)–are rigorously evaluated using a massive dataset generated from extensive raytracing (RT) simulations in a typical urban environment. Our results demonstrate that the RF algorithm outperforms other models showcasing superior predictive performance for the test dataset with a root mean square error (RMSE) of 2.38 dB. The proposed ML models demonstrate superior accuracy compared to 3GPP and ITU-R models for mmWave radio networks. This study thoroughly investigates the adaptability of these models to unseen environments and examines the feasibility of training them with sparse datasets to improve accuracy. The reduction in computation time achieved by using ML models instead of extensive RT computations for sparse training datasets is evaluated, and an efficient algorithm for training such models is proposed. Additionally, the sensitivity of ML models to noisy input features is analyzed. We also assess the importance of geometrical features and the impact of sequentially increasing the number of these features on model performance. The results emphasize the significance of the proposed geometrical features and demonstrate the potential of ML models to provide computationally efficient and relatively accurate PL predictions in diverse urban environments.