Unsupervised Machine Learning-Based User Clustering in THz-NOMA Systems

In this letter, different unsupervised machine learning (ML)-based user clustering algorithms, including K-Means, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN) are applied in non-orthogonal multiple access (NOMA) assisted terahertz (THz) networks. The key contribution of this letter is to design ML-based approaches to ensure that the secondary users can be clustered without knowing the number of clusters and degrading the performance of the primary users. The studies carried out in this letter show that the proposed schemes based on AHC and DBSCAN can achieve superior performance on system throughput and connectivity compared to the traditional clustering strategy, i.e., K-means, where the number of clusters is determined in an adaptive and automatic manner.