Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems

Hybrid precoding has been recognized as a promising technology to combat the path loss of millimeter-wave signals in massive multiple-input multiple-output (MIMO) systems. However, due to the joint optimization of the digital and analog precoding matrices as well as extra constraints for the analog part, the hybrid precoding design is still a tough issue in current research. In this paper, we adopt the thought of clustering in unsupervised learning and provide design schemes for fully-connected hybrid precoding (FHP) and adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems. For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based) scheme to explore the relevance among radio frequency (RF) chains in the optimal hybrid precoding design. The similar RF chains are merged into an individual when insufficient RF chains are available. For AHP, we propose the modified-K-means-based (MKM-based) scheme to explore the relevance among antennas. The similar antennas are supported by the same RF chain to make full use of the flexible connection in AHP. Particularly, in the proposed MKM-based AHP design, the clustering centers are updated by alternating-optimum-based (AO-based) scheme with a specific initialization method, which is capable to independently provide feasible sub-connected hybrid precoding (SHP) design with quick convergence. Simulation results highlight the superior spectrum efficiency of the proposed HAC-based FHP scheme, and the high power efficiency of the proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified to effectively handle the inter-user interference and outperform the existing work.