## 2 Related Works
It should be noted that based on the antenna radiation pattern, the gain of the antenna degrades by moving from the center of the main lobe to its boundaries. The amount of degradation depends on the design characteristics of the antenna. Hence, conventional 2D channel models, which are mainly based on the distance between transmitter and receiver, are not anymore suitable for aerial mobile communications, especially for UAV communications where the drones fly at different heights and, in the case of cellular-based communications, mainly receive signals from the boundaries of the main lobe or side lobes. Therefore, the development of 3D channel models for describing the propagation channel between UAVs and BSs is highly vital and demanding. Authors in [26] provided an insight into the propagation characteristics of the UAV-to-BS channel and proposed a statistical path loss model for suburban environments. The proposed path loss model is a function of the depression angle and the terrestrial coverage beneath the UAV. In contrast, ref. [27] followed a different approach by proposing an ML-based channel model for the UAV-to-BS scenario. The model was developed based on the received signal strength (RSS) and an unsupervised clustering algorithm, in which the drone can identify the status of the current channel without relying on statistical channel models. However, the proposed method was simply a function of distance only and did not consider the effect of the UAV's height or elevation angle. In contrast, ref. [28; 29; 30] trained different ML models for predicting either the RSS or RSRP of UAV-to-BS using a total of nine features. These features included the latitude and longitude of the UAV and the nearest BS, the ground elevation and altitude of the UAV, the ground elevation and building height of the BS, and the antenna mast height of the BS. In [29], the authors utilized multiple ensemble learning methods to predict the RSS at several heights (up to 350 m) in an urban environment. In addition, they presented a new ensemble method based on five base learners: support vector machine (SVM), Gaussian processes (GP), artificial neural network (ANN), least-squares boosting (LSBoost), and bagging. The Salp swarm algorithm (SSA) was used to integrate base learners into the new method. Results showed that the proposed ensemble method was the best, with root mean squared error (RMSE) of 6.26 dB, mean absolute error (MAE) of 3.54 dB, and mean absolute percentage error (MAPE) of 3.92%. Similarly, ref. [30] evaluated k-nearest neighbors (kNN), support vector regression (SVR), random forest (RF), AdaBoost, and gradient tree boosting (GTB) models for predicting RSRP at different UAV heights in an LTE network operating at 1800 MHz in urban and suburban environments. They introduced a new ensemble method that combines the latter base ML learners in an ensemble learning method termed voting regression (VR). The outcomes showed that the VR model outperformed the original base learners, achieving MAE of 3.227 dB, RMSE of 6.674 dB, and MAPE of 3.357%. Meanwhile, in [28], the authors presented a UAV-to-BS RSRP prediction model based on ANN for LTE networks operating at 1800 MHz frequency band in an urban environment, concerning UAV heights of up to 350 m. Combining the Levenberg-Marquardt (LM) backpropagation algorithm with self-adaptive differential evolution (DE) techniques led to the development of two novel hybrid training methods, namely the jDE and the composite DE (CoDE) algorithms. Both methods showed favorable outcomes, with CoDE-LM achieving the best. In [31], a deep ANN was used to predict RSRP and RSRQ for the UAV-to-BS communication scenario in a rural LTE network based on the spatial positioning of a UAV flying up to 180 m in altitude. This approach takes the UAV's position as input and maps them to seven training features, including latitude, longitude, altitude, azimuth, elevation, and radius.