Constrained K-means User Clustering and Downlink Beamforming in MIMO-SCMA systems

In this paper, we study the application of spatial user clustering along with downlink beamforming in multiple-input multiple-output sparse code multiple access (MIMO-SCMA) systems. A user clustering algorithm based on a constrained K-means method is proposed to limit the number of users in each cluster. Subsequently, a two-stage beamforming approach is developed in which a cluster beamformer and user-specific beamformer obtained from each stage are combined to form the final beamformer for each user. Specifically, in the first stage, the block diagonalization technique is employed to design cluster beamformers so that the inter-cluster interference is removed. In the second stage, an optimization problem is formulated to determine user-specific beamformers for all the users such that the total transmit power is minimized under signal-to-interference-plus-noise ratio (SINR) constraints. The performance of the proposed user clustering and downlink beamforming approaches in MIMO-SCMA systems is evaluated through simulations. The results provide useful insights into the advantages of the proposed scheme in terms of transmit power, and spectral efficiency over benchmark approaches.