A Balanced Complexity and Performance User Clustering Scheme for Cell-Free Massive MIMO-NOMA Systems

User clustering presents a significant design challenge when implementing non-orthogonal multiple access (NOMA) in multiple-input multiple-output (MIMO) systems. To fully exploit the potential of power domain multiplexing and achieve better performance of NOMA, users must possess significant variations in their channel gains. Consequently, it becomes necessary to cluster users based on their distinct channel gain characteristics. However, using random user clustering often leads to suboptimal outcomes, while the exhaustive search method is burdened with exorbitant complexity. To achieve the balance between system performance and complexity, this paper proposes a user clustering scheme that relies on Jaccard coefficient, which is a measure of differences in user channels, ranging from 0 to 1. A value of 0 indicates complete dissimilarity, while a value of 1 indicates complete similarity. By calculating the Jaccard coefficient, we can identify users who are dissimilar to the centroid as a cluster. Additionally, a new power allocation algorithm is developed to maximize the sum spectral efficiency by employing successive convex approximations (SCA), taking into account both spectral efficiency and user quality of service. The simulation results demonstrate that the algorithm proposed for maximizing the sum spectral efficiency exhibits a higher convergence rate and can substantially enhance the sum spectral efficiency in comparison to the full power control (FPC) scheme.