User Clustering with Spatial Concept using Supervised Learning for NOMA Downlink

Abstract

This research aims to optimize the performance of Successive Interference Cancellation (SIC) in Power Domain Non-Orthogonal Multiple Access (PD-NOMA) technology by applying spatial concepts through the use of beamforming techniques. User clustering is a key element in achieving this goal, and this research applies various supervised machine learning classification algorithms including Decision Tree, K-Nearest Neighbors (K-NN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes. The experimental results show that Random Forest achieves the highest accuracy in classifying users, followed by Decision Tree. In addition, in measuring performance using ROC (Receiver Operating Characteristic) and AUC (Area Under the Curve) curves, Decision Tree and Random Forest achieved the best results as well. While in terms of experimentation process time, decision tree has a faster time than random forest. Overall, Random Forest and Decision Tree algorithms are suitable for the task of user clustering in the context of PD-NOMA which utilizes the spatial concept of user to Base Station (BS).