The spectral efficiency (SE) and global energy efficiency (GEE) trade-off encountered in the design of millimeter-wave (mmWave)-based massive multi-input multi-output (MIMO) non-orthogonal multiple access (NOMA) networks is investigated with a particular focus on user clustering. By exploiting the similarity among user channels a pair of spectral and energy-efficient user clustering algorithms are proposed for dynamically selecting both the number of clusters and the number of users in each cluster. Subsequently, a joint analog precoder/combiner and user clustering technique is developed, followed by a multi-objective optimization (MOO) framework for flexibly balancing the GEE and SE objectives in a mmWave NOMA network subject to specific constraints. The MOO objective is initially transformed to a weighted sum rate maximization problem, followed by a quadratic-transform (QT)-based approach conceived for maximizing the non-convex objective by approximating it as a concave-convex function. Our simulation results demonstrate that the user clustering techniques designed attain a 85% performance gain over random clustering technique and demonstrating the benefits of the algorithm designed for mmWave 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.