Best Height of UAV-Aided NOMA Using ML and Optimization Techniques

Abstract

Modern communications are undergoing a historically significant shift, which is evident in a wide range of technology. One of these innovations that has significantly impacted a variety of businesses is unmanned aerial vehicles (UAVs). In the field of wireless communications, UAVs are employed to better serve users. For better performance, Non-Orthogonal Multiple Access (NOMA) technologies are included. In this research, we suggest using Machine Learning (ML) and optimum approaches to choose the appropriate height for a UAV-assisted NOMA in order to give the greatest service to consumers while taking into consideration UAV features.We find that the best performance with o.0899 average root mean square (RMSE) by Ananaya then, artificial neural network (ANN) with average RMSE 0.0931 better than ElasticNet, support vector regression (SVR), Lasso and regression (LR).