Dynamic User Clustering for mmWave-Based NOMA Networks

This study focuses on the user clustering performance of millimeter-wave (mmWave) massive multipleinput multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks. Two novel spectral and energy-efficient user clustering algorithms are proposed that exploit the similarity between user channels and condition number (CN) as the correlation index, toward dynamic selection of the number of clusters and number of users in each cluster, thus ensuring that the users in different clusters are spatially uncorrelated. Our simulation results demonstrate that the proposed user clustering techniques attain a $100 \%$ performance gain over a naive random clustering technique.