Ensemble learning techniques such as LightGBM and XGboost modesl are a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:

Faster training speed and higher efficiency,  Lower memory usage, Better accuracy, Support of parallel, distributed, and GPU learning, and Capable of handling large-scale data.

In this thesis we aim to use and optimize the two mentioned ensemble learning techniques to predict SINR in an integrated Terrestrila and Air communication Network which consist of Two base-stations, two drones and one HAPS. We use Sionna for data generation of channel based on TR 38.901 channel model.